Generic ontology based semantic business policy engine

ABSTRACT

Techniques for implementing policies. In an embodiment, first data is stored in a first data store according to a first schema. A second schema is defined based at least in part on a policy and an ontology. Second data, which includes at least a portion of the first data, is stored in a second data store according to the second schema. Storing the second data is based at least in part on a mapping of the first schema to the second schema. At least a portion of the second data is analyzed and results of the analysis are provided to a user.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/143,088, filed on Jan. 7, 2009 (TTC 021756-071101US),entitled “GENERIC ONTOLOGY BASED SEMANTIC BUSINESS POLICY ENGINE,” theentire contents of which is incorporated by reference in its entiretyfor all purposes.

BACKGROUND OF THE INVENTION

Embodiments of the present invention relate to analysis of date forenforcement of business policies.

Businesses often have internal business policies intended to address awide range of issues such as security, privacy, trade secrets, criminalactivity of employees or others with access to the business, and manyothers. These business policies address various aspects of a business,such as purchasing, selling, marketing, and internal administration.Because of the large number of activities occurring during the course ofrunning a business, which may have various entities located in a varietyof geographical locations, it is often impractical to manually monitorall activities in which improper behavior or mistakes may occur.

One approach to implementing business policies has been to monitor andcontrol computer systems used to facilitate a business's activities. Forexample, information regarding various activities, such as sales andpayroll, are often stored in one or more data stores. This informationmay be analyzed to find activity that might be in violation of abusiness policy, such as an item on an invoice or paycheck to anemployee being outside of a specified range, or a particular employeeattempting to access information to which he or she is not entitledaccess.

Typically, analyzing data requires a high level of technical expertiseas the data is often created and stored using a wide variety of businessapplications which often have differing standards and specifications,are often custom built for specific purposes, and often lack ability tocommunicate and share information with one another. Consequently, inorder to enact business policies, the expertise of those familiar withthe business applications to which the business policies are to beimplemented is often required. For instance, in order to analyze datastored in a relational database, a person may have to be able toconstruct a proper SQL statement. Generally, commonly-used applicationstypically require users to model policies in SQL, PL/SQL, or anotherapplication-specific or storage-specific language.

Those making the business policies, however, are often not the samepeople with detailed knowledge of the business' systems to which thepolicies are to be applied. For instance, a person or group of peopledeciding that, to prevent employee fraud, all payments over a specificamount should require approval by an appropriate person, may not haveany understanding how invoice data is stored in the business' systems.Such policy makers would prefer to define policies in terms that theyunderstand, such as “user”, “general ledger”, “organization”, etc., andnot in terms of the applications with which policies will beimplemented, such as “database schema x on host 55.55.55.55”, “FND_USERtable”, and “application Y”. Such policy makers would likely prefer notto take the time necessary to learn the specific application terminologyas their duties typically do not require such technical expertise.

Moreover, because businesses typically use several differentapplications to facilitate their activities, it can be burdensome forpolicy makers to learn specific terminology for several applications.Policy makers would rather prefer that they can use an intuitiveinterface in order to apply familiar terminology to create policies thatmay be applied to a variety of applications, without having to create asimilar policy for each application.

Previous applications for implementing business policies have includedapplications that work with specific business applications, and thatrequire users to have an underlying understanding of the technicaldesign of those business applications. One possible reason for this isthat database runtimes, which are frequently the underlying runtime forbusiness applications, cannot easily share runtime resources acrossinstances; and most solutions to policy modeling have either used singledatabase instances or single database connections to support theirruntime requirements.

BRIEF SUMMARY OF THE INVENTION

The following presents a simplified summary of some embodiments of theinvention in order to provide a basic understanding of the invention.This summary is not an extensive overview of the invention. It is notintended to identify key/critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome embodiments of the invention in a simplified form as a prelude tothe more detailed description that is presented later.

Embodiments of the present invention provide techniques for analyzingdata for the implementation of policies. In one embodiment, a method forimplementing policies is disclosed. The method may be performed underthe control of one or more computer systems configured with executableinstructions. In an embodiment, the method includes storing first datain a first data store according to a first schema; identifying, based atleast in part on a policy, a subset of the first data to store in asecond data store; determining, based at least in part on the policy andan ontology, a second schema for the second data store; storing seconddata in the second data store based at least in part on a mapping of thefirst chema to the second schema of the second data store, the secondschema organizing data according to the ontology and the second dataincluding at least the subset of first data; analyzing, based at leastin part on the policy and the ontology, at least a portion of the secondata to determine at least one conclusion; and providing the conclusionto a user of said one or more computer systems. In an embodiment, thesecond schema is optimized, based at least in part on the policy and theontology, for analyzing data in the second data store.

Variations of the method are also disclosed, in accordance with variousembodiments. For example, the method may include storing third data in athird data store according to a third schema, where the second dataincludes at least a portion of the third data, and where storing seconddata in the second data store is further based at least in part onanother mapping that maps the third schema to the second schema. Thethird schema may be different from the first schema. As another example,the second schema may organize at least a portion of the second datainto a collection that corresponds to a semantic concept and thatcomprises data from the first data store and the second data store. Themapping, in an embodiment, defines a correspondence from the second datato a plurality of semantic concepts of the ontology. Also, the methodmay further include selecting the mapping from a plurality of mappingsthat map at least one of a plurality of schemas to the second schema.

In an embodiment, a system for storing data is disclosed. The system, inan embodiment, includes a first data store storing first data accordingto a first schema; and at least one processor operable to define, basedat least in part on a policy, a subset of the first data to store in thesecond data store; to determine, based at least in part on the policyand the ontology, a second schema of a second data store and a mappingfrom the first schema to the second schema; and cause loading of datainto the second data store from the first data store according to themapping. The second schema may comprise a plurality of tables and theprocessor may be operable to optimize the second schema for analysisaccording to the policy. The system may also include a third data storethat stores third data according to a third schema, where the seconddata includes at least a portion of the third data and where said atleast one processor is operable to cause loading of data into the seconddata store from the third data store according to another mapping of thethird schema to the second schema. The first schema may be differentfrom the third schema. Also, the second schema may organize at least aportion of the second data into a collection that corresponds to asemantic concept and that comprises data from the first data store andthe second data store. The mapping, in an embodiment, defines acorrespondence from the second data to a plurality of semantic conceptsof the ontology. Also, the system may further include a data store thatstores a plurality of mappings that include the mapping, wherein each ofthe plurality of mappings map at least one of a plurality of schemas tothe second schema. As another example, said at least one processor isfurther operable to analyze at least a portion of the second data todetermine compliance with at least one policy.

In yet another embodiment, a computer-readable storage medium, havingstored thereon instructions for causing at least one processor to storeand analyze data is disclosed. The instructions may include instructionsthat cause said at least one processor to, based at least in part on apolicy and an ontology, identify first data to be loaded from a firstdata store to a second data store; instructions that cause said at leastone processor to define, based at least in part on the policy and theontology, a second schema for the second data store; instructions thatcause said at least one processor to direct storage of second data inthe second data store based at least in part on a mapping of a firstschema of a first data store to a second schema of second data store,the second data including at least a portion of the first data;instructions that cause said at least one processor to analyze at leasta portion of the second data to determine at least one conclusion; andinstructions that cause said at least one processor to provide theconclusion to a user of said one or more computer systems. Theinstructions may also include instructions that cause said at least oneprocessor to define, as part of the second schema and based at least inpart on the policy, a plurality of tables constructed to optimizeanalysis of the second data according to the policy.

In an embodiment, the second data includes at least a portion of thirddata of a third data store, the third data being organized by a thirdschema, and the instructions that cause said at least on a processor todirect storage of second data are based at least in part on anothermapping that maps the third schema to the second schema. The secondschema may organize at least a portion of the second data into acollection that corresponds to a semantic concept and that comprisesdata from the first data store and the second data store. Also, thefirst schema may be different from the third schema. The mapping maydefine a correspondence of the second data to a plurality of semanticconcepts of the ontology.

In an embodiment, the instructions of the computer-readable storagemedium include instructions that cause said at least one processor toselect the mapping from a plurality of mappings that map at least one ofa plurality of schemas to the second schema. Also, the instructions mayinclude instructions that cause said at least one processor to analyzeat least a portion of the second data to determine compliance with atleast one policy.

For a fuller understanding of the nature and advantages of the presentinvention, reference should be made to the ensuing detailed descriptionand accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a simplified block diagram of a computer system that may beused to practice an embodiment of the present invention;

FIG. 2 shows an example of an environment in which embodiments of thepresent invention may be practiced;

FIG. 3 shows a data set of an ontology and a matrix encoding the dataset, in accordance with an embodiment;

FIG. 4 shows the data set and the matrix of FIG. 3 in partitioned form;

FIG. 5 shows results of a map function derived from the matrix of FIG.4, in accordance with an embodiment;

FIG. 6 shows an inference library created from the map function outputof FIG. 5;

FIG. 7 shows a method for processing data according to the processdemonstrated in FIGS. 3-5, in accordance with an embodiment;

FIG. 8 shows a graphical representation for updating an inferencelibrary, in accordance with an embodiment;

FIG. 9 shows a diagrammatic representation of a logical architecture ofa semantic data store, in accordance with an embodiment;

FIG. 10 shows a diagrammatic representation of mappings betweenontological and relational meta-models that may be used with thearchitecture of FIG. 10, in accordance with an embodiment;

FIG. 11 shows a diagrammatic representation of a semantic data store andthe relationships between the semantic data store, an ontology, and anontological meta-model, where the semantic data store may be createdusing the mappings of FIG. 11;

FIG. 12 shows a diagrammatic representation of a modular reasoningsystem in accordance with an embodiment;

FIG. 13 shows a method for modularly reasoning data in accordance withan embodiment;

FIG. 14 shows a method for modularly reasoning data in accordance withanother embodiment;

FIG. 15 shows an example of a graphical representation of a policy, inaccordance with an embodiment;

FIG. 16 shows an example of another graphical representation of apolicy, in accordance with an embodiment;

FIG. 17 shows an example of yet another graphical representation of apolicy, in accordance with an embodiment;

FIG. 18 shows a method for creating policies, in accordance with anembodiment;

FIG. 19 shows an environment showing use of adaptors to store data froma plurality of data stores to a common data store, in accordance with anembodiment;

FIG. 20 shows steps of a method for analyzing data from a plurality ofdata stores that may utilize components of the environment of FIG. 19,in accordance with an embodiment.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the invention. However, it will be apparent that theinvention may be practiced without these specific details.

The following description describes an embodiment of the presentinvention in the business policy domain, and specifically withimplementing business policies using ontologies that encode businessdata. However, the scope of the present invention is not restricted tobusiness policies, but may be applied to other domains or applications.For example, any domain or application where a set of rules or criteriais used to analyze data may make use of the present invention. Examplesof domains in which embodiments of the present invention may be usedinclude segregation of duties, separation of powers, transactionmonitoring, fraud or other crime detection, semantic web applications,and generally applications dealing with large sets of data.

In general, embodiments of the present invention provide techniques forcreating policies to be applied to data. As used herein, unlessotherwise clear from context, a policy is a set of one or moreconditions and a set of one or more actions to be taken when the set ofconditions is met. For example, a policy may be that all transactions ofa certain type (such as credit card charges) over a specified amountrequire approval by a person of a specified class, such as a manager. Inthis example, the conditions of the policy are that transactions have aspecified type and amount and an action of the policy is authorizationof transactions meeting the conditions by a person of a specified class.An action of a policy may also be simply identification of data thatmeet the policy's condition(s). For example, a policy may specify thatall transactions of a certain type and over a certain amount should beidentified. In this example, the conditions are the same as in theprevious example, but the action is identification of transactionsmeeting the conditions so that, for example, a manager may review theidentified transactions and investigate any transactions he or she deemssuspicious.

Typically a policy is used to implement a business policy which is oneor more rules, guidelines, and/or principles related to the conduct of abusiness. For instance, a business policy specifying that invoices overa specific amount require manager approval may be implemented bycreating a policy that includes criteria for identifying invoices overthe specified dollar amount from information stored in one or more datastores.

In a specific embodiment, business data is encoded in an ontology andthe ontology is processed in order to ensure that business policies arefollowed. Processing the ontology involves applying graph partitioningtechniques in order to distribute the data over a plurality of reasonerinstances, where a reasoner instance is one or more processorsimplementing one or more reasoners. Typically, each reasoner instancewill comprise a single processor implementing a single reasoner,although more processors and/or reasoners may be possible in a reasonerinstance. MapReduce techniques, discussed below, may be used tocoordinate the actions of a plurality of reasoners operating over thenodes. Algorithmic matrix-based methodology is used throughout thepartitioning and reasoning process.

Turning now to the drawings, FIG. 1 is a simplified block diagram of acomputer system 100 that may be used to practice an embodiment of thepresent invention. Computer system 100 may serve as a user workstationor server, such as those described in connection with FIG. 2 below. Asshown in FIG. 1, computer system 100 includes a processor 102 thatcommunicates with a number of peripheral subsystems via a bus subsystem104. These peripheral subsystems may include a storage subsystem 106,comprising a memory subsystem 108 and a file storage subsystem 110, userinterface input devices 112, user interface output devices 114, and anetwork interface subsystem 116.

Bus subsystem 104 provides a mechanism for letting the variouscomponents and subsystems of computer system 100 communicate with eachother as intended. Although bus subsystem 104 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple busses.

Network interface subsystem 116 provides an interface to other computersystems, networks, and portals. Network interface subsystem 116 servesas an interface for receiving data from and transmitting data to othersystems from computer system 100.

User interface input devices 112 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a barcode scanner, a touch screen incorporated into thedisplay, audio input devices such as voice recognition systems,microphones, and other types of input devices. In general, use of theterm “input device” is intended to include all possible types of devicesand mechanisms for inputting information to computer system 100. A usermay use an input device in order to execute commands in connection withimplementation of specific embodiments of the present invention, such asto implement, define policies, and/or configure various components of anenterprise system, such as that described below in connection with FIG.2.

User interface output devices 114 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices, etc. The display subsystem may be a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), or aprojection device. In general, use of the term “output device” isintended to include all possible types of devices and mechanisms foroutputting information from computer system 100. Results of implementingpolicies, defining policies, and configuring various components of acomputer system may be output to the user via an output device.

Storage subsystem 106 provides a computer-readable medium for storingthe basic programming and data constructs that provide the functionalityof the present invention. Software (programs, code modules,instructions) that when executed by a processor provide thefunctionality of the present invention may be stored in storagesubsystem 106. These software modules or instructions may be executed byprocessor(s) 102. Storage subsystem 106 may also provide a repositoryfor storing data used in accordance with the present invention, forexample, the data stored in the diagnostic data repository. For example,storage subsystem 106 provides a storage medium for persisting one ormore ontologies. Storage subsystem 106 may comprise memory subsystem 108and file/disk storage subsystem 110.

Memory subsystem 108 may include a number of memories including a mainrandom access memory (RAM) 118 for storage of instructions and dataduring program execution and a read only memory (ROM) 120 in which fixedinstructions are stored. File storage subsystem 110 provides persistent(non-volatile) storage for program and data files, and may include ahard disk drive, a floppy disk drive along with associated removablemedia, a Compact Disk Read Only Memory (CD-ROM) drive, an optical drive,removable media cartridges, and other like storage media.

Computer system 100 can be of various types including a personalcomputer, a portable computer, a workstation, a network computer, amainframe, a kiosk, personal digital assistant (PDA), cellulartelephone, a server, or any other data processing system. Due to theever-changing nature of computers and networks, the description ofcomputer system 100 depicted in FIG. 1 is intended only as a specificexample for purposes of illustrating the preferred embodiment of thecomputer system. Many other configurations having more or fewercomponents than the system depicted in FIG. 1 are possible.

FIG. 2 shows a simplified block diagram of an enterprise computer system200 that may be used to practice an embodiment of the present invention.It should be understood that, generally, enterprise computer systemsvary greatly and, as a result, specific embodiments may include more orless components than shown in the figure and that the specificcomponents shown in FIG. 2 are only intended to provide an example forthe purposes of illustration.

In accordance with an embodiment, the enterprise computer system 200includes a first location 202 and a second location 204 communicativelyconnected by a network 206, such as the Internet or any suitablecommunications network or combination of networks. In an embodiment, thefirst location 202 and second location 204 correspond to separatephysical locations of a business, such as offices in two separatecities, states, or countries. While FIG. 2 shows two locations, itshould be understood that a business may have only a single location andmay include more than two locations. As shown in the drawing, theenterprise computer system 200 may include one or more user workstations208, a development server 210, and a developer workstation 212. The userworkstation 208, development server 210, and/or development workstation212 may be physically present at any of the locations, or at separatelocations. In an embodiment, the user workstation 208 and developmentserver 210 are communicatively connected to the network 206 so as toaccess various components of the enterprise computer system. Forexample, the user workstation 208 may include a browser used for viewingcontent provided from the Internet and/or from other systems within thebusiness. Further, the developer workstation 212 may be connected to thenetwork 206 through the development server 210 and may be adapted toenable certain employees within the organization to configure, install,modify, and perform other actions in connection with the business'computing systems. As an example, a developer within the organizationmay utilize the developer workstation in order to create policies thatare used to define policies and execute one or more applications thatstores data in one or more ontologies, and that reason the dataaccording to the policies in accordance with various embodiments of theinvention. Instructions for controlling the applications and the definedpolicies may be sent over the network 206 to an appropriate computingdevice executing the one or more applications.

As noted above, the first location 202 may include various computersystems used in operating the business. For example, as depicted in FIG.2, the first location 202 includes a web server 214 configured toreceive requests from various users, such as from a user of the userworkstation 208, and to respond to the requests over the network 206.While FIG. 2 shows the web server as a hardware component, as with anyof the servers described herein, the web server may also be a softwaremodule operating on a computer system. Responses from the web server 214may be provided from the web server 214 itself or through the web server214 but from a variety of sources in communication with the web server214, such as from components of an internal computer system of the firstlocation 202 or from other web servers located at other, possiblythird-party, locations.

In an embodiment, the web server 214 is communicably coupled to anapplication server 216, which is a hardware component or software moduleconfigured to run one or more applications, such as one or more policyengines and other applications for managing organizational data. As isknown, a user of the user workstation 208 may send a request to the webserver 214 that specifies a specific action to be taken in connectionwith an internal business application implemented on the applicationserver 216. The web server 214 then relays the request to theapplication server 216 which takes the specified action and returns theresult of that action to the web server 214, which in turn relays theresult to the user workstation 208. In accordance with an embodiment,the web server 214, or other component, may modify the content returnedto the user workstation 208 in accordance with one or more policiesapplicable to a user of the user workstation 208.

As shown in the example of FIG. 2, the application server 216 interactswith data stored in a first data store 218 and a second data store 220,each of which may store data relevant to the business' operation, suchas in one or more relational or other databases. While the disclosedexample shows the first location 202 having two data stores, it shouldbe understood that the first location 202 may have less than two datastores or more than two data stores. Information in the data stores caninclude a wide variety of data, such as data relating to businesstransactions, invoices, human resources data, user account data,receipts, bank account data, accounting data, payroll data, andgenerally, any data relevant to the operation of a particular business.Information from the data stores 218, 220, and other sources, may beextracted from the data stores, converted to a uniform format, andstored in an ontology in accordance with an embodiment.

In an embodiment, the second location includes its own web server 222,application server 224, first data store 226, and second data store 224which may be configured to function similarly to the identically namedcomponents above.

FIG. 7 shows a flowchart demonstrating a method 700 for processing datain accordance with an embodiment. The method 700 (or any methoddisclosed herein), for example, can include techniques described belowin connection with FIGS. 3-8. As with any method disclosed herein, themethod depicted in FIG. 7, or variations thereof and/or combinationsthereof, may be implemented by software (e.g., code, instructions,program) executing on a processor, by hardware, or combinations thereof.The software may be stored on a computer-readable storage medium, forexample, in the form of a computer program comprising a plurality ofinstructions executable by one or more processors.

In an embodiment, data is stored in an ontology by creating ontologydata from various business data sources at a data storage step 702. Asnoted below, the data can be enterprise business data or, generally, anytype of data. Storage of the data can be performed in a variety of ways.For instance, in an embodiment, a batch process is periodically executedthat causes data stored in data stores to be compiled into an ontology.For instance, data stored in a first form can be transformed using oneor more adapters configured to convert data from a first form to a formsuitable for storage in the ontology. In addition, automatic Extract,Transfer, and Load (ETL) operations from a business' data sources to asemantic data store that embodies the ontology may be defined and set torun when trigger conditions are met, such as at certain times or when acertain amount of data has been changed.

At a partitioning step 704, the ontology data is partitioned so as to bedistributable among a plurality of processors. Each processor mayimplement the same or a different reasoner instance. Partitioning thedata may include encoding the ontology data in a matrix, such as in amanner described below, and partitioning the matrix using one or morematrix partitioning techniques. In alternate embodiments, the ontologydata is not necessarily encoded in a matrix, but is distributed usingother methods. For instance, because ontologies can be represented asgraphs, such as directed graphs, graph partitioning techniques may beused. Generally, any technique for partitioning data among a pluralityof reasoners may be used.

At a distribution step 706, the partitioned ontology data is distributedamong a plurality of processors, each of which may implement ofinstances of the same or a different reasoner. Techniques, such as thosedescribed in Map Reduce: Simplified Data Processing on Large Clusters,by Jeffery Dean and Janjay Ghemawat, published at the Sixth Symposium onOperating System Design and Implementation, which is incorporated byreference for all purposes, may be used to coordinate the actions of thereasoners. In this manner, the processing of the ontology data isperformed by a plurality of reasoners so as to reduce the time necessaryfor processing. At a combination step 708, the results of the processingby the plurality of reasoners are combined into a set of processed data.Combination may include connecting results of separate processingaccording to relationships associating different sets ofseparately-processed data, such as data encoded in intersection vectors,such as those described above. Again, in an embodiment, MapReducetechniques may be used to coordinate combination the results from thereasoners.

In this manner, the work done in processing an ontology is performedefficiently and more quickly than if the ontology was processed with asingle reasoner. Other benefits in using the above method are alsoincurred. For example, the embodiments of the disclosed method allow forefficient handling of new and/or modified data, as described in moredetail below in connection with FIG. 8.

As businesses and other organizations operate, the data they storechanges as a result of business operations. New invoices are created,new payments are made to vendors, employee roles change, new people ororganizations become customers, peoples' position within an organizationchanges, and other events happen during the course of operating abusiness that may influence the addition, subtraction, or modificationof associated data. Moreover, because the amount of data stored by abusiness is typically very large, creation or modification of anontology based on the data typically takes a large amount of resourcesand, therefore, is performed as a batch process, often during times whena business' systems are under a lighter work load, such as at a time ofthe day when many employees may be at home or when most potentialcustomers are asleep.

FIG. 3 shows a representation of an example data set 300 stored in anontology, in accordance with an embodiment. It should be understood thatthe data set 300 is given for purposes of illustration and typicallydata sets for businesses or other organizations will be larger and morecomplex. In an embodiment, the data set 300 comprises a plurality oftriples 302 where a triple includes a first node, a second node, and arelationship between the first and second nodes. For example, a firsttriple 304 includes a first node A, a second node B, and a relationshipP₁ between A and B. Each node in a triple may represent a data point,such as a piece of information stored in a database of a business orused in connection with operation of such a database. Each node can be asimple piece of information such as an employee name, a particular lineitem, a particular invoice, a class of employees, or generally any typeof information or class of information that can be stored. Each node canalso represent more complicated sets of information such as completedata records or files or classes of information including attributes ofthe class.

As a concrete example, A may represent John Doe and B may represent aspecific class of employee, such as a manager. In the relationship shownin the example of the first triple 304, P₁ connecting A to B indicatesthat John Doe is a manager. As shown in the example in FIG. 3,relationships between nodes may be directional, as indicated in theexample by an arrow. For example, continuing with the example of thefirst triple 304, the arrow extending from A to B indicates that JohnDoe is a manager but not necessarily that all managers are John Doe. Anode may relate to more than one other nodes. For example, FIG. 3 showsa second triple 306, showing a relationship P₃ between the node A and anode D. Thus, considering both the first triple 304 and the secondtriple 306, it can be seen that the node A relates to both B and to D bytwo different relationships. Specifically, A is related to B by P₁ andrelated to D by P₃. For example, D may indicate a class of employeeshaving access to a particular system, such as a security system,accounting computer system, and the like. Thus, read together, the firsttriple 304 and second triple 306 indicate that John Doe is a manager,and John Doe also has access to the system represented by D. Inaddition, various nodes can be related to each other through inferredrelationships. Briefly, for example, FIG. 3 shows a third triple 308showing a relationship P₄ between nodes B and C. Continuing the examplediscussed above, C may be a specific set of accounting data for anorganization. Thus, the relationship represented by the third tripleindicates that all managers have access to the accounting data. Thus,when reading the first triple 304 and the third triple 308 together, arelationship between A and C may be inferred that John Doe has access toaccounting data because John Doe is a manager. Further details oninferred relationships are provided below.

In an embodiment, the data set 300 may be represented in a matrix. Forexample, FIG. 3 shows a matrix 310 in accordance with an embodiment. Thematrix 310, in the example shown, is formed by a series of row vectors,each row vector corresponding to a relationship of the data set 300. Asshown in the example, the order of the row vectors of the matrix 310does not have any particular significance; however, specific orderings,such as an ordering proceeding according to an index of possiblerelationships, and other orderings may be used.

Each column vector in the matrix 310 represents a node and, as with therow vectors, the columns need not be in any particular order, but maybe. Matrix 310 comprises an entry at each intersection of a row vectorand a column vector. The entries in the matrix 310 store values thatencode data set 300. In an embodiment that values for the entries inmatrix 310 are either zeros or ones. Although the example given showsentries having values of 0 or 1, other values, such as Boolean values of“true” and “false,” or generally any set of distinguishable values mayalso be used in alternative embodiments.

As noted, the columns and rows of the matrix 310 may or may not be inany particular order. For instance, in an embodiment, data is extractedfrom one or more data stores and used to construct the matrix and themanner or order in which the matrix is constructed or extracted maydictate the matrix's initial form. For instance, in an embodiment, rowsmay be added to the matrix sequentially as relationships betweenextracted data are determined. In another embodiment, columns may beappended to the matrix as each data point is examined to determine therelationships associated with the data point.

In an embodiment, a particular row includes entries of zero or one. Therelationship associated with the row may be determined by the oneentries. Specifically, a column of the matrix that intersects the row ata one entry is associated with a node involved in the relationship.Likewise, a column of the matrix that intersects the row at a zero entryis associated with a node that is not involved in the relationship.Thus, counting from the top, looking at the first row of the matrix 310which corresponds to the relationship P₁₂, the intersection between theA column and the P₁₂ row includes a zero entry thereby indicating thatrelationship P₁₂ does not involve the node A. The intersections of theP₁₂ row with columns J and I includes entries of one, indicating thatthe relationship P₁₂ involves I and J. In a like manner, ones or zeroesare filled in matrix 310 to represent the relationships represented bydata set 300.

It should be understood that, while FIG. 3 shows a matrix representationof the data set 300, other representations can be used. For example,matrices may be constructed differently than shown in the figures. Forinstance, in an alternative embodiment, row vectors may correspond tonodes while column vectors may correspond to relationships. As anotherexample, as is known, data sets stored in ontologies may be representedin a graph, a directed graph, or another representation which may encodedata differently. Techniques analogous to the techniques describedbelow, such as techniques for graph partitioning, may also be used inaccordance with the present invention. Further, it should be understoodthat while the examples in the figures show graphical representations ofspecific matrices, including the entries of the matrices, matricescorresponding to data sets will typically be too large to be displayedin the same manner, but may be stored in computer memory (eithervolatile or non-volatile) in a manner dictated by a specific applicationused to create the matrices or other representations of the data.

In an embodiment, the matrix 310 is partitioned into a convenient form,for example, by using known techniques of linear algebra. For instance,the matrix 310 may be placed into block form by using elementary rowoperations such as swapping rows. Column operations, such as switchingcolumns, may also be used. When row, column, or other operations areused, an index vector, list, or other mechanism that may be part of thematrix or stored in another location, may be updated to keep track ofwhich relationships and/or nodes correspond to each vector. For example,each entry of the first row may include information (such as a string ornumber) identifying a particular relationship and the first entry ofeach column may include information identifying a particular node. Inthis manner, when a row or column operation is performed, theidentifying information of the associated rows and/or columns areaffected by the operation in a way that keeps track of the rows and/orcolumns. As a concrete example, if the first and second rows areswitched, in an embodiment, the information identifying the first rowmoves to the second row and the information identifying the second rowmoves to the first TOW.

In an embodiment, partitioning a matrix includes arranging the columnssuch that the matrix encodes the directions of the relationships of therepresented triples. Thus, the columns may be arranged such that thecolumn corresponding to the first node in a triple is to the left of thecolumn corresponding to the second node in the triple. Otherconfigurations of matrices that encode the direction of therelationships may also be used, such as the inclusion of an additionalencoding column that includes entries that correspond to the directionof triples included in a particular row. For instance, an additionalcolumn may be added to the matrix 310 so that the intersection of a rowwith the additional column includes a 0 if the order of the columnscorresponds to the direction of the relationship encoded in the row anda 1 otherwise. For instance, the first row has a 1 in the intersectionswith the I and J columns, but the J column appears before the I column,so the order to the I and J columns does not correspond to therelationship P₁₂ extending between the I and J nodes. Therefore, in thisexample, an encoding column would have a 1 in the intersection of thefirst row with the encoding column to indicate that the relationship P₁₂extends from I to J.

In an embodiment, with the columns arranged, the rows are arranged sothat the matrix is in block form. Matrices used in accordance with thepresent invention will generally be sparse matrices because each row, inan embodiment, will have only two non-zero entries corresponding to thespecific data represented in the row. As a result, such partitioning maybe performed to form a matrix having more than one block which isconvenient for visualizing and processing of the data set 300, asdescribed more fully below.

Generally, when a matrix is used to encode data, the matrix can bepartitioned into a convenient form, such as block form, using varioustechniques. For example, spectral partitioning can be used to partitionincidence, Laplacian, or other matrices that encode a graphrepresentative of ontological data. Likewise, multilevel coarsening andpartitioning techniques, such as those that coarsen, partition, and thenuncoarsen a matrix may be used. Of course, hybrid approaches of theabove techniques and/or other techniques can be used as well.

It should be noted that such rearrangement of the columns may not bestraight forward if a data set includes a circuit, which is a set of oneor more nodes and one or more relationships arranged such that aninferred or direct relationship exists between a node and itself Forexample, a circuit exists in a situation where A relates to B, B relatesto C, and C relates to A, with the directions of the relationshipsextending from A to B, from B to C, and from C to A. With a circuit, itis not straight forward to order the columns in order to encode thedirections of the relationships without taking additional measures. Forinstance, in the circuit described above, the C column would have tooccur simultaneously before and after the A column. Nevertheless, onewith ordinary skill in the art would recognize that such situations maybe remedied through a variety of techniques. For example, a data set maybe pre-processed to locate any circuits. If any circuits are found,triples may be removed from the data set to break any circuitous paths.For instance, the triple of C to A may be removed in the example givenabove so that A does not indirectly refer to itself The removed triplesmay be separately processed and the results of the separate processingmay be combined with results of processing the modified data set.

Because the data set 300 is stored in an ontology, it can be consideredas a graph, having vertices being the nodes and the relationships beingedges. In an embodiment, partitioning a matrix representative of a dataset can be visualized by equivalent operations on a graph representingthe data set. For instance, FIG. 4 shows a representation of a graph 400of a data set such as the data set 300, above, which shows thetransitive properties of the data set. For example, if A is related to Band B is related to C, then the graph shows edges connecting B to both Aand C. As shown in the example, the graph 400 includes a first subgraph402 and a second subgraph 404. The first subgraph 402 and the secondsubgraph 404 are related to each other through the relationship P₇connecting node E, which is in the first subgraph 402, to node F, whichis in the second subgraph 404. In this manner, processing the data set300 can be performed by separately processing data in the subgraphs andcombining the results. For example, the data in the first subgraph 402may be processed in a first processor executing instructions for a firstreasoner instance, the data in the second subgraph 404 may be processedin a second processor executing instructions for another reasonerinstance, which may employ the same or a different set of rules forprocessing than the first reasoner instance. Either the first or second(or another) processor may be used to combine the results according tothe relationship P₇.

It should be understood that data sets will vary and, as a result,decomposition of a graph representing a data set will vary accordingly.For instance, a graph may be partitioned into subgraphs that aredisconnected, or may be partitioned into subgraphs that are connected toone another by more than one edge. In addition, a typical data set, inaccordance with an embodiment, will be partitioned into more than twosubgraphs which may be processed separately. Further, data in somesubgraphs may be processed in one processor, while data in othersubgraphs may be processed in another processor or processors.

Turning to the matrix representation, FIG. 4 also shows a matrix 412which has been partitioned into a convenient form. For example, thematrix 412 in an embodiment is a matrix resulting from thetransformation of the matrix 310, described above. As described above,the columns of the unpartitioned matrix 310 have been rearranged suchthat they encode the direction of the relationships between the nodes.In an embodiment, if a relationship extends from a first node to asecond node, then the column associated with the first node is placedbefore the column associated with the second node. For example, becausethe relationship P₁ extends between A and B, the A column is placedbefore the B column.

Further, the rows of the matrix 412 have been arranged so as to put thematrix in block form which, as described below, results in partitioningthe data into separately processable partitions. As discussed above,many different techniques for partitioning matrices into block form maybe used in accordance with various embodiments. As shown, the matrix 412includes a first vector set 408 (SET A) comprising the upper seven rowsof vectors and a second vector set 410 (SET B) comprising the lower sixrows of vectors, where the first vector set 408 is above the secondvector set 410. An intersection vector set 412 comprises the row vectorsthat are common to both the first vector set 408 and second vector set410. As discussed, the matrix and sub matrices of FIG. 4 are providedfor the purposes of illustration and, generally, matrices used inaccordance with various embodiments may have vector sets and submatrices having different characteristics, such as more or less rows.

As shown in the example, the first vector set 408 includes a firstsubmatrix 414 in the upper left corner that comprises entries that areeither zero or one and a first zero matrix 416 in the upper left cornerthat comprises entries that are all zero. In an embodiment, the firstsubmatrix 414 is situated to the left of the first zero matrix 416.Likewise, the second vector set 410 includes a second submatrix 418 anda second zero matrix 420 where the second submatrix 418 sits to theright of the second zero matrix 420 and the second submatrix 418includes entries being zero or one and the second zero matrix 420 havingentries all zero. In this manner, it can be seen that the partitionedmatrix 406 is partitioned into discreet blocks and may include a vectorconnecting the blocks. While the partitioned matrix 406 is composed offour block matrices and the intersection vector 412, it should beunderstood that data sets, in general, in accordance with an embodiment,will be partitioned into a larger or smaller number of blocks which mayor may not be separated by non-zero intersection row vectors. Inaddition, it should be understood that the particular positioning of theblocks of the matrix 406 is made according to mathematical conventionwith the blocks located along a main diagonal of the matrix 406, butthat other configurations are possible.

Returning to the example in the drawing, the first submatrix 414 encodesthe first subgraph 402 while the second submatrix 418 encodes the secondsubgraph 404 in the manner described above. The intersection vectorencodes the relationship between the first subgraph 402 and the secondsubgraph 404. If a graph of a data set includes two disconnectedsubgraphs, a partitioned matrix representation may not include anyintersection vectors between blocks representing the disconnectedsubgraphs. In addition, one or more row vectors of all zero entries maybe situated between blocks representing disconnected subgraphs.

In an embodiment, a map function and a reduce function are employed inorder to distribute the reasoning of an ontology among variousprocessors and to combine the results of the distributed reasoning.Reasoning an ontology may include application of a predefined set ofrules to the data of the ontology. As an example, a commonly used rulein reasoning ontologies is the transitive rule where, if node A relatesto node B and node B relates to node C, then node A relates to node C.Other rules, depending on specific applications, may be used in additionto or in place of the transitive rule. In an embodiment, the mapfunction takes as input data corresponding to a subgraph of a graphrepresenting an ontology and a set of rules to be used by a reasoner toprocess the individual triples represented in the subgraph. For asubgraph and set of rules input to the map function, the output of themap function includes data corresponding to a subgraph (typically adifferent subgraph) and an inferred vector which may encode informationabout one or more triples. In an embodiment, the subgraphs output by themap function may include nodes that are common to more than one subgraphso as to encode any relationships between subgraphs.

FIG. 5 shows a specific example of matrices resulting from a mapfunction that can be used in accordance with an embodiment.Specifically, FIG. 5 shows a first vector set 500 and a second vectorset 502 in accordance with an embodiment. In an embodiment, the firstvector set 500 encodes the first vector set 408 and intersection vector412 described above in connection with FIG. 4. Referring back to FIG. 4,the first vector set 500 encodes the first subgraph 402 and the tripleincluding nodes E and F, and the relationship P₇ extending between E andF. In this manner, the first vector set 500 encodes the first subgraph402 and the triple connecting the first subgraph 402 to the secondsubgraph 404. In an embodiment, the first vector set 500 encodes thefirst subgraph 402 and triple including E, F, and P₇ in a manner similarto that described in connection with FIGS. 3 and 4.

Similarly, the second vector set 502 encodes the second vector set 410and the intersection vector 412 described above in connection with FIG.4, thereby encoding the second subgraph and the triple represented by E,F, and P₇. As shown in FIG. 5, in an embodiment, the row vectors of thefirst set of vectors 500 are simply the row vectors of the first dataset 408 and intersection vector 412. In this manner, the map functionoutputs the first vector set 500 and second vector set 502. Subgraphscorresponding to the first vector set 500 and second vector set 502 maybe ascertained from the entries in the vector sets, as described above.In an embodiment, the first vector set 500 forms a first matrix 504which may be in block form and whose columns and rows represent nodesand relationships, respectively, as described above.

As noted above, the map function also outputs inferred vectors which mayencode the relationship between two or more nodes as determined by areasoner. For example, a set of rules may include a transitive rule foran ontology which provides, for example, that if A is related to B and Bis related to C then A is related to C. The set of rules may alsoinclude information identifying which rows should be considered whenimplementing the transitive rule. The transitive rule in processing ofontologies is convenient because when matrix representations are used,as described above, processing the transitive rule on a subgraph can beperformed using an OR operation of the relevant rows which iscomputationally efficient. In an embodiment, an OR operation on aplurality of rows is performed by performing an OR operation oncorresponding entries in the rows. For example, if the third entry ofone row is a zero and the third entry of another row is zero, an ORoperation performed on the two rows will have a zero in the third entry.If the third entry of both rows is a one, then an OR operation performedon the two rows will have a zero in the third entry. If one of the rowshas a one in the third entry and the other row has a zero in the thirdentry, then the result of an OR operation performed on the two rows willhave a one in the third entry.

In an embodiment, the inferred vectors form a set of inferred vectorswhose columns and rows encode triples as described above. For example, afirst inferred vector set 506 results from processing the first vectorset 500 according to a plurality of user-selected or predefined rules ofa reasoner. Likewise, a second inferred vector set 510 results fromprocessing the second vector set 502. In the example shown, the firstrow of the first inferred vector set 506 is a result of performing an ORoperation on the rows P₁, P₄, P₆ and P₇ of the first submatrix 504. Thisparticular operation, for instance, may be chosen by a user of thereasoner and any suitable operation or operations may be used. Likewise,the remaining rows of the inferred vector set 506 are formed usingvarious OR operations on various rows of the first submatrix 504depending on the particular rules chosen by the user. Generally, thetype of operations used to make inferred vector sets will vary dependingon specific applications and reasoners and it should be understood thatthe particular operations used to form the inferred vector sets arechosen merely as an example.

In an embodiment, a reduce function is constructed or provided whoseinput includes information about subgraphs and inferred triples fromeach subgraph. For example, the input of the reduce function may includea list of nodes directly related to nodes of the subgraph. Thus, theinput of the reduce function may include all the nodes of the subgraphas well as one or more nodes of another subgraph related to the inputsubgraph by a relationship. For example, in reference to the firstsubgraph 402 and second subgraph 404 shown in FIG. 4, the input of thereduce function may include the list {{A, B, C, D, E, F}, {F, G, H, I,J}}. In this example, the nodes A, B, C, D, E, and F are from the firstsubgraph 402 and the nodes F, G, H, I, and J are from the secondsubgraph 404. The node F is included in both lists because it is thenode in the second subgraph 404 to which the first subgraph 402 refersthrough the relationship P₇. The input of the reduce function may alsoinclude a list of inferred triples within the subgraphs, such as atriple including nodes A and E.

The reduce function determines, based upon the input, whether additionalreasoning should take place. For instance, referring to the sameexample, because the first subgraph 402 and second subgraph 404 arerelated to each other by the relationship P₇, the reduce function thentakes the inferred triples from each subgraph and applies the rules ofthe reasoner to the inferred triples input to the function and returns alist of inferences. For complicated data sets, the reduce function maybe applied repeatedly or recursively to ensure that desirable inferencesare identified. Thus, for example, the output of the reduce function mayinclude an inferred triple that includes the nodes A (from the firstsubgraph 402) and J (from the second subgraph 404), because A and J areindirectly related to one another through a series of relationships.

FIG. 6 shows an example of the output of a reduce function using theinput from the example shown in FIG. 5. In particular, FIG. 6 shows amatrix 600 (inference library) whose columns correspond to nodes andwhose rows correspond to inferred triples. The inferred triples may be,for example, a result of applying a reasoner to the first inferredvector set 506 and second inferred vector set 510 of FIG. 5. As aspecific example, the first row of the matrix 600 shows an inferencefrom A to J because there is a 1 in the intersection of the A and Jcolumns with the first row. The first row also shows the nodes involvedin an inference from A to J. For instance, the intersection of the B, C,E, F, G, I columns with the first row includes a 1 while theintersection of the D and H columns includes a 0. As a result, thematrix 600 encodes information indicating that an inference from A to Jmay include a path extending through nodes B, C, E, F, G, and I.Likewise, the fourth row of matrix 600 encodes another inference from Ato J that may include a path extending through nodes C, D, E, F, G, andI.

FIG. 8 demonstrates how embodiments of the disclosed invention allow forefficient handling of new, deleted, and/or modified data. In anembodiment, an ontology 802 representing a data set is partitioned intoa plurality of subontologies, represented in the figure by SO₁ throughSO_(n). When a piece of data of the data set is modified, one or moretriples of an ontology representing the data set change when theontology is updated. For instance, FIG. 8 shows a vector 804representative of a triple changed because of a change in acorresponding data point. Because of the partitioning of the ontology inaccordance with embodiments of the present invention, it is possible tomap the changed vector to only those subontologies affected by thechange. For example, the changed vector may be mapped to a singlesubontology, a subset of the subontologies, or all subontologies.Because the vector is mapped to only the affected subontology orsubontologies, updating the ontology may involve only updating theaffected ontologies. An inference library 806, such as the inferencelibrary represented by the matrix 600. described above in connectionwith FIG. 6, may be updated, for example by using MapReduce techniques,by updating only the portions of the reference library affected by achange in affected ontologies instead of updating the complete referencelibrary. Further, when multiple vectors representing triples change, thevectors can be likewise mapped to affected subontologies and updatingthe entire ontology can then be spread over a plurality of processors.Thus, the amount of resources spent updating an ontology based onchanged data may be significantly reduced.

FIG. 9 shows a general representation of an architecture for extractingsemantic data from various sources in accordance with an embodiment. Thearchitecture 900, in an embodiment, includes a semantic data store 902which is modeled by a business ontology 904 according to declarativedata mappings, as described in more detail below. Generally, in anembodiment, the business ontology 904 is a representation of semanticconcepts from the semantic data store 902 and the relationships betweenthose concepts. Also, in an embodiment, the semantic data store 902 maybe realized using the W3C OWL-DL ontology language. It should beunderstood that, while the present disclosure refers to various conceptswithin a business domain, the present invention is applicable to anydomain that is associated with one or more organizations that store dataas part of their operations. Therefore, while the following descriptionrefers to a business, the present invention may be applicable to anyorganization.

In the example of FIG. 9, four different data sources of a business areshown, although a business or other entity may use more than four datasources or less than four. Also, the four different data sources may bephysically realized in separate data stores, which may be in separategeographical locations, or two or more data sources may be incorporatedinto a single data store. As shown in the example demonstrated in FIG.9, a business may include a first relational database 906 which ismodeled by a first relational schema 908. As it applies to data storage,a schema is a structure configured to organize the data of one or moredata sources. For example, in a relational database, a schema thatmodels the relational database defines the tables of the database, thefields of each table, and the relationships between the fields andtables.

In the provided example, the business may also include a secondrelational database 910 which is modeled by a second relational schema912. There can be various reasons for having more than one source ofbusiness data, for example for storing data for different aspects of abusinesses' activities, such as sales and human resources. Businessesmay also store data in different forms depending on the particularapplication. For example, in FIG. 9 a light weight directory accessprotocol (LDAP) directory 914 is modeled by a LDAP schema 916. Likewise,a flat file database 918 may be modeled by a flat file schema 920. Thus,in the example shown in FIG. 9, a business may include data from avariety of sources in a variety of formats.

As can be seen in the figure, data from each of the data sources ismapped to the semantic data store 902. In an embodiment, mapping datafrom a data source to the semantic data store 902 is described in moredetail below, but generally includes extracting data from the source andloading it (or a portion of it) into the semantic data store, which mayor may not involve reformatting data from one form to a form suitablefor the semantic data store 902. In addition, mapping data from a datasource to the semantic data store 902 may involve mapping all data fromthe data source or using a filter to only map some data from the datasource. For instance, the data source may include data that is notpertinent to the purposes for which the business ontology 904 is usedand, as a result, only pertinent data would be mapped to the semanticdata store. A filter may be used to control which data is mapped to thesemantic data store. For example, the data mappings above can be used inconnection with Oracle Data Integration (ODI) Tools available fromOracle International Corporation in order to perform ETL processes thatconstrain and filter data from the various data stores and merge thedata into a common format in the semantic data store 902. As describedbelow, once maps are constructed, the maps can be used in automatedprocesses that extract data from one data store and appropriately loadthe data into the semantic data store 902. Extraction and loading ofdata can occur, for example, at predetermined intervals such as once aday, or at predetermined triggers, such as when data is changed.

As shown in the example, data from the first relational database 906 isstored in the semantic data store 902 as well as data from the secondrelational database 910, the LDAP Directory 914, and the flat filedatabase 918. In an embodiment, schemas of various data stores aremapped to the business ontology such that semantic concepts embodied inthe data stores are stored in the business ontology 904. For example,the first relational database 906 may include a plurality of tables,each table having one or more columns. The first relational schema 908may relate tables together in a useful manner, for example, relatingcustomers to invoices such that a customer is related to an invoice forgoods or services purchased by the customer. Thus, relationships definedby the relational schema 908 are mapped to the business ontology 904such that semantic concepts defined by the relational schema 908 arepreserved in the business ontology 904.

FIG. 10 shows an ontological meta-model 1000 which, in an embodiment,provides an implementation for the architecture described in connectionwith FIG. 9 and to which a relational meta-model 1002 is mapped. Theexample given in FIG. 10 may be useful, for example, for mapping datafrom a relational database to the ontological meta-model 1000 althoughit will be understood by those with ordinary skill in the art thatvarious data stores and corresponding data schemas may have differentproperties than what is shown. In an embodiment, the ontologicalmeta-model 1000 comprises a plurality of ontological storage containerswhich includes a container for ontological concepts 1004, which is asuper class of attributes 1006 class, classes 1008 class, and relations1010 class. In other words, attributes 1006, classes 1008, and relations1010 are all types of ontological concepts. In an embodiment, each class1008 is a collection of one or more entities or instances, such asemployees, and each attribute 1006 is a collection of one or moredata-type property of a class, such as a first name. As noted above, theclasses can have super classes. For example a member of an Employeeclass may be a member of a Manager class as well.

Also, in an embodiment, each relation 1010 is a binary relationshipbetween two classes. For example, a relation orgHasEmployees may be arelationship between a member of an organization class and an employeeclass. This relationship, for example, may specify employees that arepart of an organization. Relations 1010 may be further classified interms of their domains (the class or classes from which they relate) andranges (the class or classes to which they relate). Also, in anembodiment, some relations 1010 have super relations. For instanceorgHasEmployees may be a super relation of an orgHasManagers relationbecause, for example, all managers may be employees.

As shown in the diagram, the ontological meta model 1000 also includesstorage for ontological data types 1012, which may be, for example,strings, integers, floats, dates, Boolean, or other types of data. In anembodiment, data types are the ranges (value sets) of the attributes andconsist of sets of similar data forms. In the embodiment presented inthe drawings, ontological type data is stored separately from instancedata, which is stored in a hyper-denormalized relationed form. As usedherein, semantic data that is in hyper-denormalized relationed form isstored such that every attribute is stored in its own table. This formprovides an advantage in that instance data is easily and quicklyaccessible, which in turn allows for a highly distributed approach tosolve problems of inferencing, persistence, and other issues. In otherwords, the architecture in the disclosed embodiment provides the powerand flexibility of ontological storage with the performance of modernrelational database management system. However, one with skill in theart will appreciate that variations are possible and that, in othercontexts, different architecture may be appropriate. For example, onewith skill in the art would recognize that type and instance data may bestored in the same storage system and that instance data need not behyper-denormalized, but that different degrees of denormalization ofdata may be used, and different kinds of instance data may be combinedin one or more containers.

As shown in the drawing, in an embodiment, between the classes arerelations 1010 between the classes 1008 and there may be relations 1010among the relations 1010. Also, each class 1008 is an aggregation ofattributes, in accordance with an embodiment.

As noted above, the relational meta-model 1002 is mapped to theontological meta-model, as described more fully below. In an embodiment,of relational meta-model includes relational concepts 1014 which aresuper classes of tables 1016, columns 1018 and keys 1020. Also, as isknown, each table 1016 is an aggregation of columns. As can be seen,various mappings are provided between various elements of theontological meta-model 1000 in relational meta-model 1002. For instance,in an embodiment, one or more columns of a table are mapped to anattribute of the ontological meta-model 1000. Likewise, tables 1016 aremapped to classes 1008 of the ontological meta-model 1000. As keys 1020define relationships between tables 1016 in the relational meta-model,keys of the relational meta-model 1002 are mapped to relations of theontological meta-model 1010 in a manner preserving the relationshipsbetween the tables 1016. In an embodiment, relational data types 1022are mapped to a ontological data types 1012.

In an embodiment, the relational meta-model may be implemented using arelational database management system (RDBMS) and the meta-data in therelational meta-model is, therefore, readily available. The mappingshown in FIG. 10, in an embodiment, may also be achieved by utilizingApplication Programming Interfaces (APIs) exposed by the systemsimplementing the relational meta-model 1002, such as Java DatabaseConnectivity (JDBC) meta-data, Open Database Connectivity (ODBC)meta-data, and the like.

FIG. 11 shows an example fragment 1100 from a sales ontology data storethat provides an illustrative example how an ontology meta model 1102,sales ontology 1104, and semantic data store 1106 may relate, inaccordance with an embodiment. In an embodiment, the semantic data store1106 is used to store and maintain the instance data that is modeled byone or more ontologies and that has been imported, filtered, and mergedfrom the business data sources, such as those described above inconnection with FIG. 9. In an embodiment, in order to reason overtransactional data with high performance, the semantic data store 1106is formatted as a hyper-denormalized and vertically partitioned system.For example, data derived from n columns of an RDBMS table, in anembodiment, would be stored as n tables in the semantic data store 1106.

In an embodiment, the semantic data store translates policies (queries)expressed in terms of the sales ontology 1104 into queries expressed interms of the semantic store schema, and executing the translated querieson the data store. The actual execution of the query may be delegated toa reasoner. Thus, in an embodiment, a query expressed in terms ofclasses and relations will be translated by the semantic data store 1106in terms of tables and keys. For example, in an embodiment, theontological query:

ONT: SELECT X.firstName, X.lastName

will get translated into the semantic data source query:

SELECT firstName, lastName FROM Partition_(—)1, Partition_(—)2, . . . ,Partition_N.

In addition, appropriate relations may be substituted withforeign-key/primary-key pairings when the query is translated into therelational form.

As discussed above, the ontological meta-model is comprises of classes1108, relations 1110 and attributes 1112. The sales ontology 1104comprises specific instances of the members of the ontology meta-model1102. For example, as shown, the sales ontology 1104 includes severalclasses including a person class 1114, a buyer class 1116 an employeeclass 1118, an invoice class 1120 and invoice item class 1121. As seenby it's name, the person class 1114 corresponds to people such asemployees, buyers and other people. Accordingly, the buyer class 1116and employee class 1118 are sub-classes of the person class 1114. Alsoclear from its name, the invoice class 1120 may be associated withinvoices and the invoice item class 1121 may be comprised of variousinvoice items such as various products sold by a business employing thedisclosed ontology. In an embodiment, the employee class 1118, invoiceclass 1120, and invoice item class 1121 have corresponding tables in thesemantic data store 1106. Other classes of the sales ontology 1104 mayalso have corresponding tables in the semantic data store 1106.

As shown, the sales ontology 1104 includes various relations from therelations 1110, such as a buyerOf relation 1122 and a sellerOf relation1124 and a hasItems relation 1126. The names of the various relationsalso may be related to their semantic meaning For instance, as can beseen in the figure, a buyer of the buyer class 1116 may be related to aninvoice of the invoice class 1120 by the relation buyerOf because thebuyer may have purchased the particular items of the invoice. Likewise,an invoice of the invoice class 1120 is related to invoice items of theinvoice item class 1121 by the relation hasItems 1126 because theinvoice items were included on the invoice. Also, the sellerOf relation1124 relates an employee of the employee class 1118 to an invoice of theinvoice class 1120 when the employee was the person who sold the itemslisted on the invoice. In an embodiment, relations 1110 are representedin the semantic data store 1106 by the pairing of the primary key of thetables of the semantic data store 1106, as discussed below.

Further, various items of the sales ontology 1104 may include variousmembers of the attribute class 1112. As an example, person 1114 mayinclude a first name 1128 and a last name 1130, which, as indicated inthe drawing, may be stored as strings. Likewise, a buyer 1116 may have abuyerID unique to the buyer as may an employee 1118 have an employeeID1134 unique to the employee. Continuing this example, the invoice 1120may include an invoiceID 1136 unique to the invoice 1120 and a date1138, for example, on which the invoice 1120 was created. As a finalexample, an invoice item of the invoiceItem class 1121 may include anamount corresponding to the price at which the associated item was soldto the buyer 1116.

As discussed above, various items of the sales ontology are stored in asemantic data store 1106. In an embodiment, the semantic data store 1106may closely resemble a data store of another data model such as arelational database model. Thus, in an embodiment, the semantic datastore 1106 includes a plurality of tables where each table correspondsto a class of the ontology meta-model 1102. It should be understood,however, that the example semantic data store 1106 shown in the drawingsmay be in an intermediate format used to facilitate transformation ofthe data. Data from the semantic data store 1106 may be furthertransformed, for example, into a format suitable for use with aparticular reasoner operable to reason the data.

Thus, as shown in the illustrative example of FIG. 11, the semantic datastore 1106 includes an employee table 1142, an invoice table 1144 and aninvoice item table 1146. Each of the tables of the semantic data store1106 may include a key comprising an attribute unique to the entitiesrepresented by the table. For instance, the employee table 1142 mayinclude a column having each employee ID 1132. Other attributes may alsobe stored in tables such as the last name and first name attributes ofemployees in the employee table 1142. Likewise, the invoice table 1144may include a column corresponding to an invoice ID primary key,employee ID foreign key and buyer ID foreign key such that in thismanner, for example, an ID of an invoice may be located in the invoicetable 1144 and the employee associated with the invoice and buyer towhich items on the invoice are sold may be identified.

The above embodiments, and variations thereof, provide include featuresadditional to those discussed above. FIG. 12 shows an embodiment 1200 ofan environment in which embodiments of the invention may be practiced.As shown, the environment 12 includes a plurality of data stores 1202from which data is extracted and stored in a semantic data store 1204,such as in a manner described above. In an embodiment, data stored inthe semantic data store 1204 is reasoned by a reasoner 1206, which isoperated by a user of a user terminal 1208, in accordance with anembodiment. While FIG. 12 shows the reasoner 1206 analyzing data from asingle semantic data store 1204, the reasoner 1206 may utilize data frommultiple semantic data stores as well as from one or more of the datastores 1202, or data from other sources. Generally, the semantic datastore 1204 is a data store whose persistence mechanism can be the filesystem, database, or memory, depending on the usage within theapplication and, as described above, may be dynamically optimized forstorage of data based on deployed semantic domain definitions. Asdescribed above, semantic domain definitions may be OWL files thatencapsulate domain taxonomy, which may be defined by a business expert,for a particular domain.

In addition, it should be noted that FIG. 12 shows a simplifiedenvironment for the purposes of illustration, but that actualimplementations may utilize various components in addition to that whichis illustrated and, in various embodiments, certain components areomitted. For example, in an embodiment, the reasoner 1206 is implementedin one of several layers of a software architecture adapted forenforcing policies. As an example, an interface, such as a Web 2.0interface, may be provided for users to utilize various components.Customized interfaces for utilizing various components may be createdusing REST Web Services or services using other protocols, such as SOAP.One or more interfaces may be used to operate application services whichcoordinate components of policy enforcement, such as a policy enginethat operates the reasoner 1206 and the semantic data store 1204, dataservices that coordinate the transfer of data to the semantic data store1204, and report services that provide reporting documents based onanalysis of data in the semantic data store 1204, and the like. In anembodiment, the data services utilize Oracle Data Integrator 11gR1 andthe report services utilize Oracle Business Intelligence Publisher, bothavailable from Oracle Corporation.

As shown in the drawing, the reasoner 1206 comprises a plurality ofreasoning modules, where each reasoning module is configured to apply aset of rules to analyze data in the semantic data store 1204. While FIG.12 shows a certain number of reasoning modules of the reasoner 1206, thereasoner 1206 may have more or less reasoning modules. When reasoningdata, the reasoner may use all or some of its reasoning modules,depending on the type of reasoning being conducted. For instance, a usermay, through an input device, define a particular analysis that the userwould like to perform, and the reasoner may use one or more applicablemodules, such as in a manner described below. Further, a user may definehis or her own reasoning modules, which may include a combination ofsome of the reasoning modules of the reasoner 1206 directed to analyzedata in a particular manner.

In an embodiment, the reasoner includes one or more pattern basedreasoning modules 1210 (abbreviated as PBRM) and one or more semanticreasoning modules 1212 (abbreviated as SRM). In an embodiment, a PBRM1210 is a sub-reasoner of the reasoner 1206 that uses a predefinedprocess for performing statistical analysis on data from the semanticdata store in order to infer information from the data. PBRMs mayutilize range reasoning where data is looked at over a specified range,such as over a specified time period. As an example, utilizing amatrix-based approach, such as the approach described above, acovariance matrix of a vector may be constructed in order to measure howthe changes of variables in the vector depend on others. Likewise, thecovariance of two variables may be measured for other objects, such asmatrices or higher-dimensional objects. Correlation between twoseemingly random variables, such as between invoice amounts and paymentsunrelated to the invoices, may signify fraud. A PBRM may take as input aset of data, such as a sampling of numerical values (such as invoiceline items) over a time period, and may output conclusions based on astatistical analysis of the numerical values, such as covariancematrices or other objects.

Other statistical techniques may be used in PBRMs. For instance, patternrecognition may be used to identify activities that are out of theordinary. As an example, certain invoices, payments, and or other itemsmay be flagged for review if they contain an amount that is above orbelow a predefined threshold. As another example, pattern recognitiontechniques may be used to flag invoices, payments, or other items thatare not necessarily above or below a threshold, but that are otherwiseabnormal, such as invoice amounts that are larger or smaller than usual,but not outside of a range that would cause any flags to be set. Patternrecognition may also be used to compare activity with activity of thosehaving similar duties. For instance, pattern recognition may be used toidentify, through analysis of purchases and/or other data, that amanager of a location is replacing parts on equipment more frequentlythan managers of other locations. An investigation may be subsequentlymade to determine whether the manager is legitimately acting differentlyfrom his or her peers, whether corrective action needs to be taken,and/or whether fraud is being committed, such as by profiting off thesale of used parts.

Generally, techniques that may be employed in PBRMs include:cross-correlation analysis to discover the relationship between multipledependent variables; Bayesian filters to look at past events and buildprobabilistic models to predict future events to detect whether past,present, and/or future events violate a policy; and wavelets fordetection of data that is most likely to be suspect. Other techniquesmay also be used and, as new techniques are developed, a user may definereasoning modules that are able to apply any given technique. Forinstance, in an embodiment, users may define techniques that may beemployed by a PBRM using combinations of the above techniques and/ordefining additional techniques.

One or more SRMs 1212 may be used in connection with one or more PBRMsin order to increase the effectiveness of the modules. In an embodiment,an SRM is a reasoning module that applies one or more rules to a set ofdata, which may be put into matrix form, as described above, in order toprovide information about the relationships among the various data. Forinstance, a semantic reasoning module may identify all invoices relatedto a particular employee. Generally, use of SRMs and PBRMs providesincreased flexibility in choosing the data to be analyzed and thetechniques to be used for analysis. For instance, output of one or moreSRMs may be used as input for one or more PBRMs. As an example, if JohnDoe is an employee, a SRM may be used to identify invoices issued byJohn Doe, such as using any of the techniques, or variations thereof,discussed above. One or more covariance techniques may be used by one ormore PBRMs to determine whether there is a correlation between theinvoice amounts and other data, such as data not associated with JohnDoe. An SRM may be used to exclude data from the analysis that typicallywould be correlated to the invoice amounts, such as payments to thevendors identified on the invoices. An SRM may take input objects fromthe semantic data store, may construct appropriate matrices, and mayperform matrix operations on the matrices depending on the nature of thereasoning being performed, although matrices may be input into SRMs inother embodiments. Output from an SRM may be a set of inferences, orother conclusions, about the relationship among semantic data, or may bea set of numerical values (such as invoice line items), or other data.

Likewise, the output of one or more PBRMs may be used as input to one ormore SRMs. For instance, as discussed, PBRMs may be used to findcorrelations among various data. A SRM may be used to provide usefulinformation about data having correlations, such as people, roles,vendors, and others associated with a particular datum. This informationmay be viewed by an analyst who may decide whether to investigatefurther and/or take corrective action. Additionally, the information maybe used in order to define rules for additional analysis. The reasoner1206 may include additional logic to coordinate the flow of data amongreasoning modules being used, such as by formatting output of onereasoning module into a format suitable as input for another reasoningmodule. For instance, if an SRM outputs a set of inferences, thereasoner 1206 may extract from a semantic data store objects (such asnumerical values corresponding to objects associated with theinferences) and provide those values to a PBRM for processing by thePBRM.

As discussed in the preceding paragraphs, SRMs and PBRMs may be used inseries (where output of one or more modules is used as input for one ormore other modules). SRMs and PBRMs may also be used in parallel inappropriate circumstances. For instance, output of an SRM and output ofa PBRM may together be used as input for one or more other modules, eachof which may be an SRM or PBRM. Additionally, while the above discussionpertains to SRMs and PBRMs, other types of modules may be employed. Inan embodiment, one or more hybrid modules may be used in ways discussedabove, where a hybrid module is a reasoning module that employs bothsemantic reasoning (such as transitive reasoning of semantic data) andstatistical reasoning (such as pattern-based reasoning of numericaldata). A hybrid module may comprise a combination of one or more SRMsand/or PBRMs in series and/or parallel.

FIG. 13 illustrates steps of a method 1300 for reasoning data, inaccordance with an embodiment. As with any method disclosed herein, orvariations and/or combinations thereof, the method 1300 may be performedunder the control of one or more computer systems configured withexecutable instructions. The executable instructions may be embodied ona computer-readable medium. In an embodiment, at a data storage step1302, data is stored in one or more data stores, such as during normaloperations of an organization, as described above. As noted above, datamay be stored in a plurality of data stores that utilize various methodsof storing data, such as methods for storing data in a relationaldatabase, in flat files, and the like.

In accordance with an embodiment, at a semantic data storage step 1304,at least a portion of the data stored in the one or more data stores isstored in a semantic data store, such as a semantic data storeconfigured as described above. As discussed, storing data in thesemantic data store may involve the use of various filters in order toexclude some data from the one or more data stores and also may involvethe use of various transformations of the data that put the data in aform suitable for storage in the semantic data store, such as in amanner described above. In addition, while the method 1304 describes asingle semantic data store, more than one semantic data store may beutilized.

At a semantic reasoning step 1306, in an embodiment, data from thesemantic data store is reasoned using a SRM, where the SRM may be asdescribed above. For instance, a SRM may apply transitive reasoning todata in the semantic data store in order to identify relationshipsspecified by a user of a system employing the method 1300, such as allinvoices associated with a particular employee and/or having particularattributes. Semantic reasoning may include construction of one or morematrices or other objects whose entries signify something in the data,such as an amount, or a 0 or 1 as described above. Once the matrix ormatrices are constructed, semantic reasoning may include applying matrixoperations and/or other analysis to the matrices, depending on theparticular type of reasoning being performed. At a statistical reasoningstep 1308, data from the semantic data store is reasoned using a PBRM,in accordance with an embodiment. For instance, a PBRM may applystatistical reasoning to data specified by a user, such as to particularinvoice values for the invoices identified by the SRM. As with thesemantic reasoning step 1306, the statistical reasoning step may includeconstruction and/or operations and/or other analysis on one or morematrices whose entries have a significance to the data.

While the method shows the semantic reasoning step 1306 performed beforethe statistical reasoning step 1308, the steps may be performed inanother order or at the same time. For example, a PBRM may be used toidentify suspicious values in the Semantic data store and a SRM may thenidentify employees and other semantic objects associated with thesuspicious values. Further, also described above, a plurality of SRMsand/or PBRMs may be used to reason data in the semantic data store andmay reason data in series and/or in parallel. Also, reasoning modulesother than SRMs and PBRMs may be used as well. In order to providecustomizability and/or scalability, each reasoning module may beadaptable to receive as input from other reasoning modules. Forinstance, operations in an embodiment where matrices are used, such asthose described above, the dimensions of a matrix output by a reasoningmodule are used by another reasoning module so that operations on thematrix by the other reasoning module proceed properly. The dimensionsmay vary based on the amount or other characteristics of data beingreasoned.

In an embodiment, at a results step 1310, results of the reasoning areprovided to the user. Providing the results may include causing thedisplay of information corresponding to the results through a graphicaluser interface of the system. The results may be presented in variousforms which may employ text, graphics, video, audio, and other features.For example, graphs that illustrate statistical relationships betweensemantic objects may be displayed, as may text describing therelationships.

FIG. 14 shows a method 1400 for directing reasoning of semantic data, inaccordance with an embodiment. Steps of the method shown in FIG. 14 maybe used, for example, to allow a user to specify which data should beanalyzed and which reasoning modules should be used. Thus, steps of themethod shown in FIG. 14 may be used to allow a user to specify how themethod shown in FIG. 13 (or variations thereof) should be performed. Themethod 1400 may be performed pursuant to interaction of a user with agraphical user interface, such as an interface having one or more of thefeatures described below, where the interaction may involve use by theuser of one or more input devices. At a data identification step 1402,data is identified in accordance with an embodiment. For instance, auser may specify, through an interface, data in which he or she isinterested and, based at least in part on input from the user,appropriate data is identified. Identifying data may include specifyingtypes of semantic objects, such as invoices, and identifyingcorresponding data in the semantic data store, such as specific invoicescreated by employees of an organization. One or more attributes ofsemantic object types may also be specified, such as ranges for valueson invoices, employees or a certain rank, and the like.

In an embodiment, at a SRM selection step 1404, an SRM is selected.Selection of the SRM may be based at least in part on user input, whichmay be received during performance of the data identification step 1402.For example, if a user specifies that he or she would like to analyzeall invoices belonging to a particular employee or group of employees,an SRM configured to identify invoices associated with the employee(s)may be selected. At a PBRM selection step 1406, in an embodiment, a PBRMis selected. As with the SRM, selection of the PBRM may be based atleast in part on user input. For example, if a user specifies that he orshe would like to analyze the correlation between invoice values andother semantic objects, a PBRM operable to perform this analysis may beselected. For instance, a PBRM that constructs a covariance matrix fromvectors in a matrix constructed in accordance with the above descriptionmay be selected.

While FIG. 14 shows the SRM selection step 1404 occurring before thePBRM selection step 1406, the steps may be performed in another order orat the same time. In addition, as discussed above, embodiments of thepresent invention provide for scalability. Accordingly, the method 1400may include selection of a plurality of SRMs and/or a plurality ofPBRMs. In other embodiments, one or more SRMs are selected but no PBRMsare selected. Likewise, only one or more PBRMs may be selected withoutselection of any SRMs.

At a data reasoning step 1408, the identified data is reasoned accordingto the selected SRMs and PBRMs, in accordance with an embodiment.Reasoning the data may include applying any selected SRMs and PBRMs inan order that is based at least in part on user input. At a results step1410, results of the reasoning are provided, such as in a mannerdescribed above.

As discussed above, users may interact with an interface in order todefine the way in which data is analyzed in order to ensure compliancewith one or more policies. As an example, a user may interact with aninterface in order to define how to detect whether fraud is beingcommitted or is potentially being committed. In an embodiment, usersspecify parameters that define how analysis of data is to take place.Parameters may be defined using semantic concepts, such as employee,invoice, line item, and the like. The interface may operate according toexecutable instructions embodied on a computer-readable storage medium.

As an example, in accordance with an embodiment of the presentinvention, FIG. 15 shows an example graphical representation 1510 of ananalysis to be performed as part of implementation of a policy relatedto credit card charges, specifically a policy relating to credit cardcharges over $500. Upon receipt of instructions from a user, a systemperforming analysis of data according to the graphical representation1510 may direct a semantic reasoning module to identify from a semanticdata store credit card charges on company credit cards that are foramounts greater than $500. Identification of the credit card charges maybe performed according to techniques described above, or othertechniques. This graphical representation may be used, for example, forthe implementation of a business policy specifying that all credit cardcharges greater than $500 require approval from a specific person orclass of persons, such as managers. Identification of such credit cardcharges using the policy allows for implementation of the businesspolicy.

The graphical representation 1510 includes a credit card charges object1512 that includes a plurality of options for specifying data that maybe related to credit card charges. The specified data may be identifiedduring analysis performed during implementation of the policy. Forexample, a date checkbox 1514 allows users to specify, by checking thedate checkbox 1514, that credit card charges identified duringimplementation of the policy will include date information about thedate on which the charge was made or recorded. Likewise, a descriptioncheckbox 1516 and an amount checkbox 1518 allow users to specify thatcredit card charges identified during implementation of the policy willinclude a stored description of each charge and/or an amount of eachcharge, respectively.

In various embodiments, users are able to specify various criteria sothat implementation of a policy results in the identification ofinformation matching or closely matching the criteria. For example,continuing the example of FIG. 15, the credit card charges object 1512includes an amount comparison field 1520 that allows users to specifythat credit card charges that are to be identified by implementation ofthe corresponding policy should have amounts matching certain criteria.In the example shown, the amount comparison field 1520 includes anamount condition dropdown box 1522 and an amount entry field 1524 whichcollectively allow a user to enter an amount into the amount entry field1524 and specify, using the amount condition dropdown box 1522, whatamount identified credit card charges should have in comparison with theamount entered into the amount entry field 1524. In the example shown, auser has selected that each credit card charge identified by the policyshould have an amount greater than 500 dollars. In various embodiments,credit card charges not matching the selected criteria may be identifiedby implementation of a policy, such as when few or no results match theselected criteria. For example, continuing the example shown, ifimplementation of the policy corresponding to the graphicalrepresentation 1510 does not result in any credit card chargesidentified, credit card charges having amounts less than but near 500may be included. In accordance with an embodiment, fields may be addedto or removed from graphical objects, as appropriate.

As noted above, various objects may be associated with one another, forexample by graphically linking the objects together, for variouspurposes. For instance, the graphical representation 1510 includes anemployee object 1526 which, in the example shown FIG. 15, has beenassociated with the credit card charges object 1512 by connecting theobjects together with a line. In this specific example, the results ofassociating the employee object 1526 with the credit card charges objectare that a credit card charge identified by implementation of the policyincludes information identifying the person who made the charge, such asthe name of the employee which may be specified by a name checkbox 1528of the employee object 1526. Thus, when the policy associated with thegraphical representation 1510 is executed, an SRM may identify from asemantic data store credit card charges having the specified properties.A second SRM may take the identified credit card charges as input andidentify names of the employees that made the identified charges. Thecredit card charges and names may be associated with one another in oneor more data records, such as in a table in a relational database. Also,the credit card charges and names may be displayed to a user (such as ina table, spreadsheet, or other format) and/or may be used as input intoanother reasoning module, such as a PBRM or another SRM, depending ondirections from a user.

As seen in the drawing, the credit card charges object includes a“delete” button, a “test” button, and a “save” button. Other elements ofan interface employing embodiments of the present invention may includethese buttons, and/or similar buttons or other elements that perform thesame and/or similar functions. In an embodiment, the “delete” buttonallows a user to delete the policy, thereby disallowing access to thepolicy and/or removing the policy from computer memory. The “test”button, in an embodiment, allows a user to analyze data according to theparameters that he or she specified. For instance, selection of the“test” button in FIG. 15 may cause a computer system to analyze data ina semantic data store in order to identify credit card charges greaterthan $500 and display the identified charges with their associateddates, descriptions, and amounts. As discussed above, data in thesemantic data store may be stored in a relational database. In thisinstance, selection of the “test” button may result in appropriatequeries being made to the database to identify data according to thedefined analysis. Further, one or more matrices may be defined andappropriate matrix operations may be performed. Also, selection of the“test” button may cause only a portion of a data store to be analyzed,which may be useful when complete analysis would take a long time due tothe size of the data store but the user simply wants to determinewhether the analysis that he or she designed results in desiredinformation being returned.

Moving on to the “save” button, in an embodiment, the “save” buttonallows a user to save the graphical representation, or other informationcorresponding to the graphical representation, in computer memory, whichmay be non-volatile. The policy may be saved in memory as a set ofinstructions that instruct a computer system to perform an analysis ofthe data according to specified parameters. A user may access a savedanalysis from memory and analyze data according to the policy and/or mayutilize the policy in connection with other policies. For instance, auser may utilize techniques described herein in order to use a policy asa component in another analysis and/or to modify the analysis.

FIG. 16 shows another example of a graphical representation 1630 createdby a user in order to create an analysis, in accordance with anembodiment. The graphical representation 1630 includes a purchase orderobject 1632 which has features similar to the credit card charges object1610, described above. For example, the graphical representation 1630includes a plurality of checkboxes 1634, the selection of whichspecifies information to be included with purchase orders identified byimplementation of the policy.

The graphical representation 1630 shown also includes fields forselecting criteria for various pieces of information associated withpurchase orders. Boolean operators are also included in order to providesubstantiality for how the criteria are selected. For example, in theexample of FIG. 16, an amount comparison field 1636 and a line itemcomparison field 1638 are selected and connected together with an ANDoperator so that criteria selected with the amount comparison field 1636and with the line item comparison field 1638 both must be matched orclosely matched during implementation of the policy. Other operators,such as OR operators or other Boolean or non-Boolean operators may alsobe included. Various controls for arranging fields, such as a deletecontrol 1640 for deleting fields, may be included to provide robustfunctionality for creating policies. Other controls, such as a “test”control 1642 for testing a created analysis, and a “save” control 1644for saving an analysis, may also be included.

In accordance with various embodiments, other features are included foruser-definition of analyses performed in connection with implementationof policies. For instance, in accordance with an embodiment, variousgraphical objects corresponding to data analysis techniques are includedso that a user may include one or more of the graphical objects into agraphical representation of an analysis to be performed as part ofimplementation of a business policy so as to indicate that the dataanalysis technique should be applied during implementation of thepolicy. As an example, an icon representative of an algorithm fordetecting micropayment fraud may be placed onto a graphical object, suchas an object representative of an invoice, to indicate that thealgorithm should be applied A plurality of graphical objectsrepresentative of commonly-used data analysis techniques may be includedfor selection by a user. In addition, users may create their own dataanalysis techniques or modify and/or combine data analysis techniques inorder to create custom data analysis techniques.

Accordingly, FIG. 17 shows an example of an interface 1700 of a tool fordesigning analyses used during implementation of policies, in accordancewith an embodiment. The interface may behave according to executableinstructions embodied on a computer-readable storage medium. In theexample shown, the interface 1700 is divided into three verticalcolumns, although other arrangements are possible and, in an embodiment,the arrangement is changeable according to user input. As shown, themiddle column is labeled as “Work Space” and the right column is labeledas “Pattern Palette.” Also as shown, the left column includes two rows,the upper row being labeled as “Predefined Semantics” and the secondbeing labeled as “Custom Semantics.” In the Predefined Semantics row, aplurality of semantic elements are provided as selectable interfaceelements. In an embodiment, the predefined semantics may includegraphical representations of semantic objects that are provided for usewith common software programs. For instance, in the example shown,predefined semantics are included for E-Business Suite application(EBS), PeopleSoft (PSFT) available from Oracle Corporation, andQuickbooks Enterprise available from Intuit, Inc. As shown in theexample, predefined semantics may be provided through interface elements(shown as cubes having a plus sign or a minus sign) which may beselected to show the predefined semantics for the associated software(by selecting a cube with a plus sign) or to hide the predefinedsemantics for the associated software (by selecting a cube with a minussign).

In an embodiment, the semantic elements in the Predefined Semantics rowcorrespond to data items that are commonly used when enforcing policies.For instance, in the example shown, the semantic elements associatedwith EBS include a customer element, an employee element, and invoiceelement. Elements may also include sub-elements. For instance, in theexample shown, the invoice element includes elements commonly associatedwith invoices, such as a line item sub-element, a purchase ordersub-element, a sales person sub-element, and a vendor sub-element.

A user may interact with the elements on the interface 1700 in variousways. For instance, a user may use a mouse or similarly operationalinput device to select an element and drag the element into theWorkspace column (i.e. the middle column labeled as “Work Space”). Upondropping the item into the Workspace column (for instance by releasing amouse button), a box corresponding to the element may appear in theWorkspace column. For example, an Invoice box 1702 may appear in theWorkspace column upon dragging and dropping an Invoice element from thePredefined Semantics row of the left column into the Workspace column.The box may include elements associated with invoices, as describedabove.

In the Custom Semantics row of the left column, in an embodiment, theinterface may include one or more elements (tools) that allow a user todefine custom semantics, such as by labeling items in a data store thatdo not correspond to any of the predefined semantics or that docorrespond to one of the predefined semantics, but where thecorrespondence is not automatically recognized. Users may also definecustom semantics using the tools provided in order to define analysisfor policies, such as in a manner described above. In the example shown,the custom semantics includes two categories of custom semantics, a“Mappings” category and an “Entities” category. In an embodiment, theMappings category includes tools for mapping data from various datasources to semantic objects. For instance, as shown, the Mappingscategory includes a flat-file mapper for mapping data from flat-files, aRDBMS mapper for mapping data from relational databases, and a custommapper for mapping data from other data sources. Each of the mappers,when selected, may provide an interface for identifying data from one ormore data sources. Software providing the interface may utilize an APIof the data source in order to gain access to the data and the interfacemay allow developers to input commands, according to an API, which arenot pre-loaded with the software. As an example, software providing aninterface of the RDBMS mapping tool may utilize the API of a particularRDBMS to gain access to tables of a relational database. A user mayspecify, for example, that data in a particular column of a particulartable correspond to a particular semantic object. For instance, the usermay specify that data in a column identify customer names. In anembodiment, once mappings are made using any of the tools in theMappings category, the mappings may be saved and semantic entitiesmapped to data sources may appear appropriately in the PredefinedSemantics row.

Tools in the Entities category, in an embodiment, provide for buildinganalyses for policies using various semantic objects. For instance, apredicate tool, in an embodiment, allows one to specify an associationbetween two semantic entities such that, when data is analyzed accordingto an analysis that has been defined, data that has the specifiedassociation is identified. Graphically, the predicate tool connects twographical objects representative of semantic entities with a line orother device representative of an association. In the example shown inthe Work Space column, an Invoice object is connected to a Sales Personobject with a line and the Sales Person object specifies the name ofsales person. In this manner, when data is analyzed according to theexample arrangement of graphical objects defined in the Work Spacecolumn, invoices that are identified will be associated with a salesperson (or perhaps several sales people) whose name is Bob. In anembodiment, if the checkbox next to “name” in the Sales Person graphicalobject is not checked, then invoices would be identified as well assales people associated with the identified invoices, regardless oftheir name. In a similar manner, a Lineitem graphical is shown asconnected to the Invoice graphical object with a line, therebyspecifying that lineitems for identified invoices should be identified.In this manner, a user may specify the types of information he or shewould like to view in connection with any identified invoices.

Another tool in the Entities category, in an embodiment, is a Group toolwhich, allows a user to specify that certain semantic objects are partof a group such that one or more actions may be taken with respect tothe group. In an embodiment, the Group tool allows users to graphicallysurround a plurality of graphical objects in order to specify thatsemantic objects represented by the graphical objects are part of agroup. For instance, in the Work Space column, the Sales Persongraphical object and the Lineitem graphical object are surrounded by arectangle having a dashed border, thereby indicating that sales peopleand line items applicable to the defined analysis are part of a group.In the example shown, an icon labeled FHT has been superimposed onto theborder defining the group, indicating that a Fast Hough Transform (FHT)should be computed for the data associated with the grouped graphicalobjects. In an embodiment, the FHT icon is superimposed onto the borderof the group through a drag and drop operation by a user from anotherlocation on the screen, as described below, although any type of userinteraction with the interface may be used in addition, or as analternative to a drag and drop. Further, the FHT icon (or any of theother icons that may be used, described more completely below) may beassigned to a group through other actions, such as by a user indicating(perhaps through a drag and drop) that the FHT icon should appear on theborder of the group, in the space surrounded by the group, or throughany other specified user action.

Also in the Entities category, in an embodiment, a Classifier toolallows users to define new semantic entities or to modify existingsemantic entities. For example, if a company sells widgets, “widgets”may not appear as a predefined semantic entity, but it may wish todefine one or more analyses that utilize data related to its widgets. Inan embodiment, upon selection of the Classifier tool, the user isprovided with an opportunity to create or modify a semantic entity.Creation and/or modification of the semantic entity may involveproviding a name to the entity and specifying which attributes theentity should have. In addition, a user may be able to define the datatypes of the attributes of a semantic entity (such as integer, double,string, and the like) and/or the data types may be determined based on amapping of the semantic entity to a data source (which may be completedusing one of the mapping tools discussed above). For instance, if acolumn in a RDBMS contains integers and that column has been mapped toan attribute of a created entity, then the attribute of the semanticentity may automatically be assigned an integer data type.

As discussed above, various types of statistical analysis may beperformed for data represented by graphical objects. In an embodiment,the Pattern Palette includes a plurality of graphical icons, eachrepresentative of a type of analysis that may be performed. Forinstance, as discussed above, the pattern palette includes an FHT iconfor performing Fast Hough Transforms. In addition, a Calculator tool1702 may be provided for performing more simple analysis, such asaddition, subtraction, multiplication, division, and the like, amongdata corresponding to one or more of the graphical objects in the WorkSpace column. For instance, the Calculator tool may be used to identifythe difference between list prices and sale prices for items identifiedaccording to an analysis defined in the Work Space column. Ifapplicable, such as with the Calculator tool, a user may be providedcontrols that allow the user to select or otherwise define how the toolbehaves. The controls may be provided automatically upon selection ofthe tool or may be provided upon one or more specified user actions withthe graphical icon representative of the tool and/or other interactionswith the interface.

The Pattern Palette, or other portion of a user interface, may includeother graphical representations of analyses that may be performed ondata represented by graphical objects selected and/or grouped by theuser. For example, graphical representations, such as icons or otherobjects, may be provided for each of the statistical analyses discussedabove and/or for user-defined analyses. Further, in another embodiment,a user may group graphical representations of semantic objects in theWork Space without using the Grouping tool discussed above by dragging agraphical representation of an analysis around the graphicalrepresentations to be grouped, or in other ways.

FIG. 18 shows a method 1800 for creating policies, in accordance with anembodiment. The method shown in FIG. 18, or variations thereof, may beimplemented by software (e.g., code, instructions, program) executing ona processor, by hardware, or combinations thereof. The software may bestored on a computer-readable storage medium, for example, in the formof a computer program comprising a plurality of instructions executableby one or more processors.

In an embodiment, the method 1800 includes providing a graphical objectsrepresentative of semantic objects to a user at an object providing step1802. For example, one or more computer systems may cause display of agraphical user interface that a user may interact with using an inputdevice of the computer system(s) in order to cause the graphical objectsto appear and/or the interface may include a plurality of displayedgraphical objects that the user may select and/or move using the inputdevice. The graphical objects may be similar to those illustrativeexamples described above, although their appearance may vary. Inaddition, graphical objects representative of particular types of dataanalysis, such as those described above, may be provided as well.

In an embodiment, at an arrangement receipt step 1804, an arrangement ofgraphical objects is received. Receiving the arrangement of graphicalobjects may include receiving a series of commands from the user via theinput device, where the series of the commands indicates which objectsare received and how they are graphically arranged on a display deviceof the user. For instance, referring to the illustrative example of FIG.17, the graphical arrangement of objects in the Work Space column mayhave been produced according to user interaction with the interface.Receiving the arrangement of graphical objects may also includereceiving data that indicates how the graphical objects have beendirected to be arranged by the user. In addition, the arrangement mayinclude one or more graphical objects representative of particular typesof analysis, such as pattern or other statistical analysis, as describedabove.

At a conversion step 1806, in an embodiment, the arrangement isconverted to executable instructions for performing analysis that may beimplemented, such as in a manner described above. For instance,executable instructions for execution by an application may be generatedbased at least in part on the arrangement. Conversion of thearrangement, in an embodiment, includes identifying a set of conditionsfor data fulfilling the conditions to be identified upon execution ofthe policy, such as data within specified amounts and/or data associatedwith semantic classes or specific semantic entities. Also, conversion ofthe arrangement may include construction of executable instructions forimplementing the policy based at least in part on the arrangement.Conversion of the arrangement may also include identification of one ormore actions to be taken for data that fulfill the conditions, such asdisplay of the data in one or more formats, messages to be sent tospecified people and/or to be displayed, and the like.

As discussed above, graphical representations of analysis may specifydata to be analyzed, where that data may be from various sources. Inaddition, also discussed above, various techniques may be used to movedata from various data stores used by various applications during anorganization's operations. Accordingly, FIG. 19 demonstrates anenvironment 1900 in which analysis of data from various data stores maybe achieved. While the environment 1900 is shown having particularcomponents for the purpose of illustration, an actual environment inwhich the invention is practiced may have more or fewer components thanwhat is shown.

As shown in FIG. 19, in an embodiment, data generated by a plurality ofbusiness applications 1902 may be stored among a plurality of datastores 1904. The business applications may include applications such asapplications such as Oracle E-Business Suite applications, PeopleSoftEnterprise applications, Siebel, JD Edwards, and others. As discussedabove, some of the data stores 1904 may utilize schemas that differ onefrom another. One data store may utilize a relational database schema,another data store may utilize a flat file schema, and other data storesmay utilize other schemas. Accordingly, in an embodiment, a set 1906 ofindividual adaptors 1908 is used to collect data from a plurality ofdata stores. In an embodiment, an adaptor is a set of executableinstructions that cause a computer system (or combination of computersystems) to read data from one data store and load it into another datastore according to a mapping of the data stores' schemas. An adaptor mayinclude instructions for performing appropriate ETL transformations fromone schema to another, such as ETL operations to move data from variousdata stores to a semantic data store, such as described above inconnection with FIGS. 9-11. For example, an adaptor may map a column ofa relational database table to another relational database table of adifferent database. In an embodiment, adaptors are specific todata-source type rather than to instance. Thus, if an organizationutilizes multiple instances of a single application, a single adaptor isall that is necessary to extract data from data stores for each of theinstances, although more adaptors can be used.

In accordance with an embodiment, the each of some of the adaptors 1908maps a schema of one of the data stores 1904 to the schema of a semanticdata store 1910 that is utilized by a reasoner 1912 that analyzes thedata in the semantic data store 1910. The semantic data store 1910 mayorganize data according to a schema such as a schema in accordance withthat described above in connection with FIGS. 9-11, although it mayutilize a different schema. Generally, as discussed, the semantic datastore, in an embodiment, stores data in a manner that realizes anontology by associating data according to their semantic relationshipsto one another.

For example, an adapter may map columns of a relational database to alocations in the semantic data store that corresponds to an appropriatesemantic concept, such as invoices, customer identification numbers, andthe like. As another example, names of employees in an LDAP directorymay be mapped to a location in the semantic data store that correspondsto employees. In this manner, when data in a data store is updatedthroughout operations of an organization, the updated data can beextracted from the data store, transformed to an appropriate format (theformat used by the semantic data store, in an embodiment), and loadedinto the semantic data store.

In an embodiment, software that includes executable instructions forperforming functions disclosed herein may be provided with a pluralityof pre-configured adaptors that are operable to extract data generatedby commonly used applications, including some of those listed above.Also, in an embodiment, users of such software can create their ownadaptors to extract data generated by applications for whichpre-configured adaptors are not provided. For instance, manyorganizations build (or have built) their own applications which storedata in a particular way specific to the application. Pre-configuredadaptors may also be customized by users to better control which data isextracted from an organization's data stores. Adaptors (pre-configuredor custom) may be created and/or modified using a suitable ETL tool,such as Kettle Pentaho Data Integration available from PentahoCorporation. organizations have at their disposal pre-configuredadaptors for common applications they have purchased as well ascustom-built applications.

When the data from the various data stores has been loaded into thesemantic data store, a user may utilize a user terminal 1914 in order todirect an application to analyze data in the semantic data store. Theuser terminal 1914 may be communicably connected to the reasonerdirectly and/or over a communications network, such as an intranet orthe Internet. A user may, for example, define how analysis should beperformed in a manner in accordance with that described above.

FIG. 20 shows steps of a method 2000 that may be used in connection withthe environment shown in FIG. 19 in order to analyze data. In anembodiment, data is stored in a first data store at a first data storagestep 2002. The first data store may hold data generated as a result ofoperation of one or more applications. For example, the first data storemay organize data according to a logical schema that is used inconnection with an application that is used by an organization. Thelogical schema may be related to operation of a relational database orother data storage system different from a relational database, such asthose discussed above. While the drawing shows the first data storagestep with respect to a single data store, the step may include storingdata in a plurality of data stores. As discussed above, an organizationmay utilize several applications during its operations where theapplications may utilize separate data stores which may utilizedifferent logical schemas to organize the data.

In an embodiment, at a map definition step 2004, a mapping from thefirst data store to a second schema of a second data store defined. Inan embodiment, the mapping is defined based at least in part on the datarelevant to one or more policies according to which analysis of data inthe second data store will be performed. Thus, in an embodiment,defining the mapping includes identifying data from the first data storethat is relevant to one or more policies and, therefore, that should bestored in the second data store. The one or more policies may have beenselected by a user from a collection of predefined policies, or may havebeen custom made according to instructions from a user.

In an embodiment, the method includes defining the second schema of thesecond data store according to the data from the first data store (andfrom other data stores, if applicable) that are identified as relevantto the one or more policies. Defining the second schema may be performedas part of the map definition step 2004. In an embodiment, the secondschema is defined based at least in part on a policy according to whichdata in the second data store will be analyzed. The second schema mayalso be defined based at least in part on an ontology relating to thedata in the second data store. In an embodiment, the second schema isdefined as a collection of relational tables that associate datarelevant to the policy according to corresponding semantic objects of anontology, such as that in the semantic data store, described above.Other schemas may be used as the second schema.

Continuing with the example of a second schema that comprises tables,the tables of the second schema may be defined to have a particulardegree or normalization according to the type of policy analysis to beperformed. For instance, in an embodiment, certain types of analysis areassigned a predefined degree of normalization for the data that is to bestored in the second data store from the first data store (and/or fromother data stores). For instance, in an embodiment, policies related toauthorization (such as whether an employee is acting beyond the scope ofhis/her authorization) result in the second schema beinghyper-normalized (or, generally, relatively more normalized) whereaspolicies related to transactions result in the second schema beinghyper-denormalized (or, generally, less normalized, having more dataredundancies). Other types of policies, such as for configurationpolicies, prevention policies, and the like, can each require their owndegree of normalization in the second data store. In this manner, thesecond data store is tuned for performance according to the type ofanalysis to be performed in connection with the data in the second datastore. Generally, the second schema may be defined in any manner that issuitable for analysis of data in the second schema.

As discussed above, policy analysis can be user-defined and can spanseveral areas, such as authorization, prevention, configurations,prevention, and the like. In such situations, or in general, the secondschema may be defined according to the data relevant to a policyanalysis to be performed. For instance, as discussed, data from thefirst data store is identified with corresponding semantic concepts ofan ontology. Thus, the ontological concepts applicable to a policy maybe used in order to determine a score for how the second schema shouldbe defined. For example, transactional concepts such as invoices andpayments may be weighted in favor of denormalization while otherconcepts such as “has access” and “system” may be weighted in favor ofnormalization. Accordingly, in an embodiment, the weights of theconcepts relevant to policy analysis is used to determine how normalizedor denormalized tables of the second schema should be.

Defining the second schema may include a mapping selection step, amapping from the first data store to the second data store is selectedfrom a collection of pre-defined mappings. Selection of the mapping maybe performed by referencing a table that indexes adaptors according tothe data stores to which they map. The selected mapping may map thelogical schema of the first data store to semantic objects of anontology modeled by seethe second schema of the second data store. In anembodiment, the mapping is selected by selecting an appropriate adaptorfrom a plurality of adaptors that map a different logical schema to thesecond logical schema of the second data store. Further, in instanceswhen data from a plurality of data stores is to be analyzed, a pluralityof adaptors may be selected, where each adaptor includes a mapping fromone of the plurality of data stores to the second schema of the seconddata store.

At a second data storage step 2006, in an embodiment, data from thefirst data store (or from a plurality of data stores, if appropriate)are stored in the second data store according to the mapping. Forexample, one or more ETL operations may be performed in order to extractdata from the first data store, transform the data into a formatsuitable for the second data store, and then load the data into thesecond data store. The second data storage step, in an embodiment, isperformed as a batch process during appropriate times. For instance, inan embodiment, the second data storage step is performed on a dailybasis at a time when use of the first data store by one or moreapplications is relatively light, although the step can be performed atother times. Further, if data is loaded into the second data store froma plurality of data stores, the second data storage step may beperformed at different times for each of the data stores. Also, the datastorage step may be performed over a period of time and not necessarilyas a batch process. For example, data from the first data store may beloaded to the second data store at times defined by one or moretriggers, such as when data in the first data store is created orupdated. Loading of data into the second data store from the first datastore may be performed in response to one or more events that arepublished by an event subscription service executing in connection withthe first data store.

As noted, the second data store may organize data in a manner thatmodels an ontology. Accordingly, in an embodiment, the second datastorage step includes extracting data from the first data store andloading the data into the second data store in appropriate locationsthat correspond to appropriate semantic concepts. For example, using theexample of a semantic data store described above, information thatidentifies an invoice in the first data store may be stored in thesecond data store in a location dedicated to invoices. Likewise,attributes of an invoice may be stored in an appropriate locationdedicated to invoice attributes. Thus, a line item of an invoice may bestored in a table dedicated to invoice line items. Relationships betweendata in the first data store as preserved by the first logical schema,in an embodiment, are preserved in the second logical schema.Accordingly, if a line item is associated with a particular invoice inthe first data store, the line item will be associated with the invoicein the second data store.

As discussed, information identifying a semantic object may be stored inmore than one data store of an organization. For example, dataidentifying an employee may be stored in a data store of an accountingsystem as well as in a data store of a human resources system. The dataidentifying a semantic object in one data store may be different fromdata identifying the semantic object in another data store. An employeeidentifier, for example, may be a unique number assigned to an employeein one data store and another unique number assigned to the sameemployee in another data store. Accordingly, in an embodiment, thesecond data storage step may include transforming data referring tocommon semantic objects into a format suitable for the second datastore. Thus, a single semantic object (such as a person) may have asingle identifier in the second data store regardless of how manyidentifiers the semantic object has throughout the data stores of anorganization. An index or other organizational structure may bemaintained in order to keep track of which identifiers are equivalent toother identifiers in other data stores.

At a data analysis step 2008, in an embodiment, data in the second datastore is analyzed. Data analysis may be performed pursuant to one ormore of the techniques described above, although other techniques may beused. A user of a system that is operable to perform the method 2000,for instance, may define the analysis to be performed in connection withenforcing one or more policies, as described above. For instance, a usermay specify one or more conditions and/or sets of conditions that, whenmet, indicate violations of a policy. In an embodiment, the dataanalysis step 2008 is performed according to continuous controlsmonitoring or continuous auditing techniques. For instance, the data inthe second data store may be analyzed according to one or moreconditions and/or sets of conditions on the data in order to determinewhether a policy has been violated. Analysis may be performed uponupdates of the data in the second data store, such as when an ETLoperation is performed that results in a change in the second datastore, although analysis may be performed more often. In this manner,near real-time detection of policy violations is achieved.

In an embodiment, at a conclusion providing step 2010, results of theanalysis are provided to one or more users. Thus, a message or otherdisplay of information (such as a graph, table, or chart) reflectingresults of the analysis is provided to a user, such as by display on ascreen viewed by the user. In an embodiment, providing results of theanalysis is predicated on one or more events. For instance, in anembodiment, results of the analysis are provided upon analysis of thedata in the second data store indicating that one or more policies havebeen violated, although results can be provided at other times, such asupon user requests for results of analysis regardless of whether apolicy has been violated. In this manner, proper persons and/or systemsare notified when policy violations occur, thereby allowing for quickand appropriate responses.

Although specific embodiments of the invention have been described,various modifications, alterations, alternative constructions, andequivalents are also encompassed within the scope of the invention.Embodiments of the present invention are not restricted to operationwithin certain specific data processing environments, but are free tooperate within a plurality of data processing environments.Additionally, although embodiments of the present invention have beendescribed using a particular series of transactions and steps, it shouldbe apparent to those skilled in the art that the scope of the presentinvention is not limited to the described series of transactions andsteps.

Further, while embodiments of the present invention have been describedusing a particular combination of hardware and software, it should berecognized that other combinations of hardware and software are alsowithin the scope of the present invention. Embodiments of the presentinvention may be implemented only in hardware, or only in software, orusing combinations thereof.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims.

Other variations are within the spirit of the present invention. Thus,while the invention is susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in the drawings and have been described above in detail. It shouldbe understood, however, that there is no intention to limit theinvention to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructions,and equivalents falling within the spirit and scope of the invention, asdefined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the invention (especially in the context of thefollowing claims) are to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. The term “connected” is to beconstrued as partly or wholly contained within, attached to, or joinedtogether, even if there is something intervening. Recitation of rangesof values herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein can be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate embodiments of the invention and does not pose a limitationon the scope of the invention unless otherwise claimed. No language inthe specification should be construed as indicating any non-claimedelement as essential to the practice of the invention.

Preferred embodiments of this invention are described herein, includingthe best mode known to the inventors for carrying out the invention.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate, and the inventors intend for the invention to be practicedotherwise than as specifically described herein. Accordingly, thisinvention includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the invention unlessotherwise indicated herein or otherwise clearly contradicted by context.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

What is claimed is:
 1. A computer-implemented method of implementingpolicies, comprising: under control of one or more computer systemsconfigured with executable instructions: identifying a query, the querybeing based at least in part on a policy that comprises one or moreuser-defined conditions based at least in part on one or more semanticconcepts of an ontology; identifying a subset of the first datasatisfying the query to store in a second data store, the first databeing stored in a first data store according to a first schema;determining, based at least in part on the policy and the ontology, asecond schema for the second data store; storing second data in thesecond data store based at least in part on a mapping of the firstschema to the second schema of the second data store, the second schemaorganizing data according to the ontology and the second data includingat least the subset of first data; analyzing, based at least in part onthe subset of the first data satisfying the query, at least a portion ofthe second data to determine at least one conclusion, the analyzingcomprising defining the second schema based at least in part on a typeof analysis of the subset of the first data to be performed on thesecond data in the second data store; and providing the conclusion to auser of said one or more computer systems.
 2. The computer-implementedmethod of claim 1, wherein the method further includes storing thirddata in a third data store according to a third schema, wherein thesecond data includes at least a portion of the third data, and whereinsaid storing second data in the second data store is further based atleast in part on another mapping that maps the third schema to thesecond schema.
 3. The computer-implemented method of claim 2, whereinthe second schema organizes at least a portion of the second data into acollection that corresponds to a semantic concept and that comprisesdata from the first data store and the third data store.
 4. Thecomputer-implemented method of claim 2, wherein the first and the thirddata stores are used to support one or more enterprise businessapplications, and wherein the policy is defined independently of thefirst and the third schemas.
 5. The computer-implemented method of claim1, wherein the second schema comprises a plurality of tables and whereinsaid determining the second schema includes optimizing the schema forsaid analyzing.
 6. The computer-implemented method of claim 1, furthercomprising selecting the mapping from a plurality of mappings that mapat least one of a plurality of schemas to the second schema.
 7. Thecomputer-implemented method of claim 1, wherein the identifying thesubset of the first data further includes storing all first datasatisfying the one or more conditions in the second data store.
 8. Thecomputer-implemented method of claim 1, wherein the storing second datafurther includes storing all first data satisfying the one or moreconditions in the second data store according to the second schema.
 9. Asystem for storing data, comprising: one or more processors; and memoryincluding executable instructions that, when executed by the one or moreprocessors, cause the one or more processors to collectively at least:identify a query, the query being based at least in part on a policythat comprises one or more conditions defined by a user based at leastin part on one or more semantic concepts of an ontology; define a subsetof the first data satisfying the query to store in a second data store,the first data being stored in a first data store according to a firstschema; determine, based at least in part on the policy and theontology, a second schema of a second data store and a mapping from thefirst schema to the second schema; cause loading of data into the seconddata store from the first data store according to the mapping; andanalyze, based at least in part on the subset of the first datasatisfying the query, at least a portion of the second data to determineat least one conclusion, the executable instructions to analyze compriseexecutable instructions that further cause the one or more processors todefine the second schema based at least in part on a type of analysis ofthe subset of the first data to be performed on the second data in thesecond data store.
 10. The system of claim 9, further comprising a thirddata store storing third data according to a third schema, and whereinthe second data includes at least a portion of the third data andwherein said at least one processor is operable to cause loading of datainto the second data store from the third data store according toanother mapping of the third schema to the second schema.
 11. The systemof claim 10, wherein the second schema organizes at least a portion ofthe second data into a collection that corresponds to a semantic conceptand that comprises data from the first data store and the third datastore.
 12. The system of claim 10, wherein the first schema is differentfrom the third schema.
 13. The system of claim 10, wherein the first andthe third data stores are used to support one or more enterprisebusiness applications, and wherein the policy is defined independentlyof the first and the third schemas.
 14. The system of claim 9, whereinthe second schema comprises a plurality of tables and wherein said atleast one processor is operable to optimize the second schema foranalysis according to the policy.
 15. The system of claim 9, furthercomprising a data store that stores a plurality of mappings that includethe mapping, wherein each of the plurality of mappings map at least oneof a plurality of schemas to the second schema.
 16. The system of claim9, wherein the identifying the subset of the first data further includesstoring all first data satisfying the one or more conditions in thesecond data store.
 17. The system of claim 9, wherein the storing seconddata further includes storing all first data satisfying the one or moreconditions in the second data store according to the second schema. 18.A computer-readable storage medium, having stored thereon instructionsfor causing at least one processor to store and analyze data, theinstructions including: instructions that cause said at least oneprocessor to identify a query, the query being based at least in part ona policy that comprises one or more conditions defined by a user basedat least in part on one or more semantic concepts of an ontology and theontology; instructions that cause said at least one processor toidentify a first data satisfying the query to be loaded from a firstdata store to a second data store; instructions that cause said at leastone processor to define, based at least in part on the policy and theontology, a second schema for the second data store; instructions thatcause said at least one processor to direct storage of second data inthe second data store based at least in part on a mapping of a firstschema of a first data store to a second schema of second data store,the second data including at least a portion of the first data;instructions that cause said at least one processor to analyze at leasta portion of the second data to determine at least one conclusion basedon the first data satisfying the query, the instructions to analyzecomprising instructions that cause said at least one processor to definethe second schema based at least in part on a type of analysis of thefirst data to be performed on the second data in the second data store;and instructions that cause said at least one processor to provide theconclusion to a user of said one or more computer systems.
 19. Thecomputer-readable storage medium of claim 18, wherein the second dataincludes at least a portion of a third data stored in a third datastore, the third data being organized by a third schema, wherein saidinstructions that cause said at least one processor to direct storage ofsecond data are based at least in part on another mapping that maps thethird schema to the second schema, and wherein the second schemaorganizes at least a portion of the second data into a collection thatcorresponds to a semantic concept and that comprises data from the firstdata store and the third data store.
 20. The computer-readable storagemedium of claim 19, wherein the first and the third data stores are usedto support one or more enterprise business applications, and wherein thepolicy is defined independently of the first and the third schemas. 21.The computer-readable storage medium of claim 18, wherein the seconddata includes at least a portion of a third data stored in a third datastore, the third data being organized by a third schema, wherein saidinstructions that cause said at least one processor to direct storage ofsecond data are based at least in part on another mapping that maps thethird schema to the second schema, and wherein the first schema isdifferent from the third schema.
 22. The computer-readable storagemedium of claim 18, wherein the instructions include instructions thatcause said at least one processor to define, as part of the secondschema and based at least in part on the policy, a plurality of tablesconstructed to optimize analysis of the second data according to thepolicy.
 23. The computer-readable storage medium of claim 18, furthercomprising instructions that cause said at least one processor to selectthe mapping from a plurality of mappings that map at least one of aplurality of schemas to the second schema.
 24. The computer-readablestorage medium of claim 18, further comprising instructions that causesaid at least one processor to analyze at least a portion of the seconddata to determine compliance with at least one policy.
 25. Thecomputer-readable storage medium of claim 18, wherein the identifyingthe first data further includes storing all first data satisfying theone or more conditions in the second data store.
 26. Thecomputer-readable storage medium of claim 18, wherein the storing seconddata further includes storing all first data satisfying the one or moreconditions in the second data store according to the second schema.